The Premise

As a thought leader and having a fondness for promoting innovation, I would throw down two principle and fundamental requirements that will challenge your status quo, to actually improve your decision making processes. The first is the requirement to build or provide for a broad linked data infrastructure in your enterprise. This vision is clearly the essence of a company called Zitgist, LLC. The second is the requirement to take a new perspective on a financial model that supports the notion of "Knowledge Capital"¹ as trademarked by Paul A. Strassmann.

Key Performance Indicators (KPI) most often associated with Corporate Performance Management (CPM) are the subject of many marketing and sales presentations by companies peddling proprietary solutions. Some firms have paid dearly for specialized Business Intelligence applications. So, why do companies not see better results? What are the KPIs not telling them about their business and why? I postulate it has to do with decision processes. Decisions are not just made, rather many things go into making decisions and it is these decision processes that could be vastly improved.

Foundations in Decision Process Improvement (DPI) is the conjugation of these concepts. The marriage between the CPM tools that provision KPI data (including information communication techniques i.e., early warning) and an enterprise that has complete integrated access to all data across the enterprise are crucial in order to achieve DPI on an unparalleled scale. Suffice it to say following along with or in conjunction with data integration is interoperability. The Institute of Electrical and Electronics Engineers (IEEE) defines interoperability as:

"the ability of two or more systems or components to exchange information and to use the information that has been exchanged."²

This has been the Holy Grail of the Information Age. You say, "It has never been done before", should be your first reaction. The remainder of this white paper will focus on the foundation aspects to better DPI and the why and how these concepts come together.

Business Intelligence

KPIs come in many forms from simple to complex information formats to highly distilled dashboards and perhaps very stimulating visualizations. Business Intelligence (BI) systems have been the IT industry buzz word now for quite awhile. Far too often, the indicator provides a static value but does not carry with it a certain degree of knowledge that either communicates or infers a reason for the number. The number might represent good or bad news. Sales were up in Utah and flat in Georgia. In many cases the reported statistic while current is shown within a control chart with upper and lower limits. The chart gives a sense of "we must do something" if the number is trending toward a limit month after month. The consumer of the KPI may be required to make a decision but in many cases it is not clear why all of a sudden sales went up in Utah. So the decision is made to call a meeting. When decisions are made a consequence is a result. A factor regarding why the results are not better is directly related to the level of knowledge available when the decision maker encounters the KPI. Ideally, the decision should come from the end user executive charged with making such decisions in his/her area. Instead of calling a meeting where focus might end up on Georgia’s sales figures, the decision process should have been robust enough to support another path. The executive should have alternatively decided to check that enough product is in the pipe line to supply the Utah sales territory. A savvy business man or woman may instinctively take the right decision in this case. However, be that as it may and the distractions that flare up in day to day operations such pathways are easily missed.

Decision Streams

Another facet about decisions and the impact the process has on results is the fact that no single decision in a typical day when made correctly or optimally necessarily will reflect on performance. Many good decisions whether small, medium or large over a quarter, season or campaign should and will have a positive exponential effect on the management of corporate performance. Think of decisions in terms of good and bad streams on the order of a laminar flow water jet. When all the molecules are going in the same direction parallel to each other it is a beautiful thing. A series of bad decisions is the opposite, having negative consequences for CPM.   

Are firms doing accounting on the countless decisions that are made each day? Are firms tracking decisions in an aggregated statistical manner over time within the context of categorical attributes such as criticality, impact/risk, relationships, Geo Proximity, valuation, outcomes, etc.? Perhaps only the US military does this in their training and real-time evaluations of the battle space.

Much depends on the type of decision, whether you are talking about programmed or non-programmed decision making activities. There are many good slide presentations on the Web for a general overview. No matter what the decision environment or scope, better information and knowledge brings about better decisions. In most any business today the Occupational Safety and Health Administration (OSHA) has special emphasis on a domain within CPM. In practical terms, a firm that has no policies and procedures in place and decides to implement a corporate wide program, is probably a good decision. It is a good decision because by statistically demonstrating and achieving high standards on an annual basis, liability insurance premiums will stay low, a KPI in CPM. So, the decision to go forward with initiatives causes a cascade of decisions in the firm from top to bottom. Decision accounting comes in the form of monitoring. For example, it might be decided that security and safety audits would be conducted in all areas. As a result of this decision, employees document or count all security issues and safety hazards. The information gathered would cause management to refine their decisions and modify the policy to require 55% of all the problems uncovered will be remediated within 55 days. What good does it do find a problem if you do not fix it? Thus, the statistics trickle back up the chain of command whereby the CPM group sets an ambitious goal for site safety of less than or equal to 10% total recordable incidents computed using the US Department of Labor‘s formula http://www.bls.gov/iif/osheval.htm for the year. It is no stretch of anyone’s imagination to know that if you make your workplace a safer place, you would expect to have fewer employees injured on the job.

These decision streams seem very concrete and straight forward. Of course each year management wants to see this KPI go lower. They might decide to spend money training employees on safety in the areas they work. At some point the statistics may stall. In other words no progress is being made as the year goes on. Unfortunately, the OSHA statistics in the CPM dashboard yields no clues as to why the site is not meeting the more stringent goals. So the question goes out and down the chain of command speculating that somehow the increasing number of recordable incidents is related to the increasing number of first aids reported, more or less as a hunch. This is where the knowledge is missing. The number does not carry with it any notion of correlation. The consumer of this KPI is left undecided as to what to do. Some might say this level of information recorded on the production floor or any business unit for that matter is too granular. In reality, employees may be recording these events but the knowledge is locked up in weekly PowerPoint Presentations. We say it is locked up because it is in a digital form that is not structured dataA. It is not machine to machine processable and to extract the kind of information that we need at the CPM level. To answer the hunch is manually labor intensive.

In this scenario, decision process improvement may be the mechanism that drives a firm toward zero recordable safety incidents. In order to do this though, the firm must embark upon building knowledge assets. In this case, data recorded on the number of first aids, their type, severity and frequency per type will be better stored as structured data. The asset grows over time from month to month as records are maintained for trending and review. The knowledge asset will be linked to other safety and security knowledge assets, such as near misses, recordable incidents and security.

The Asset

A knowledge asset as a term or concept may have a different frame of reference for those familiar with knowledge engineering. This is so because they are not thinking within the same context of the linked data infrastructure and semantic acculturation proffered by the community of people involved in the research and development of the Semantic web. For example, a person may develop a knowledge asset by writing a paper that is a reference to a comprehensive body of information focused on a particular issue. This may include an overview of the topic, implications for policy, research results, landmark studies; and charts and graphs to clearly communicate the scope of the issue. The asset may contain links to other resources and leading researchers in the field. 

In this paper, when we are talking about a knowledge asset, we are beyond data and information. As Tim Berners-Lee says,

            "It is about making links, so that a person or machine can explore the web of data. With linked data, when  you have some of it, you can find other, related, data." ³

It is literally a web, the analog to a spider web, most often thought of in terms of the World Wide Web, but within the scope of this discussion, derived from graph theory in mathematics or computer science. The theoretical behind the concepts is a mathematical structure used to model pairwise relations between objects from a collection. To be less abstract, the definition of Linked Data used internally by Zitgist follows:

            "Linked Data is a set of best practices for publishing and deploying instance and class data using the RDF  data model, naming the data objects using uniform resource identifiers (URIs), and exposing the data for access via the Http protocol, while emphasizing data interconnections, interrelationships and context useful to both humans and machine agents."⁴

We may draw upon a comprehensive body of information and subject matter experts, but in practice linked data requires the Resource Description Framework (RDF)B that models first order predicate logic. As Mike Bergman describes in his AI3 blog "What Is Linked Data?":

            "RDF and First Order Logic (FOL) are powerful because of simplicity, ability to express complex schema and relationships, and suitability for modeling all extant data frameworks for unstructured, semi-structured and structured data."⁵

Semantic Knowledge Assets

Decision Process Improvement is necessarily dependent on the Semantic Knowledge Asset. Thus, we use this term to differentiate our transformation. At the CPM operational level, knowledge realization may be an extracted fact, a skilled resource or an inferential awareness that is the turn key for a decision that must be a transmutation in to goods and services a customer is willing to pay for, otherwise knowledge and its use in decision making would have little value. It is this very notion that may be the reason decision support systems are less than optimal both in terms of timeliness and precision.

As an initiative to commence the development of Semantic Knowledge Assets (SKA), business owners must accept the second requirement as stated in the 1st paragraph. The idea is that SKA(s) must truly become assets on the corporate balance sheet. Current accounting practices are capital and labor models. Presently, few firms methodically value knowledge assets even though companies create or generate knowledge. The natural aggregation of a valuated SKA(s) is knowledge capital if it is to be a recognized reportable line item in financial statements. In Strassmann’s 1998 paper "The Value of Knowledge Capital"⁶, he sites the example of Abbott Laboratories accumulation of Knowledge Capital. This exactly parallels with my thinking in terms of mind set to grow your knowledge capital. Knowledge today is paid for and expensed against current profits. Strassmann suggests that such expenses should be treated as the investment in knowledge capital.

To build the enterprise linked data infrastructures, initiatives are going to have to come from the top as a strategic objective. The lower echelons of personnel in the corporation may recognize the potential, but lack the resources and leadership to enable to get it to percolate to the top in most cases. At the department level, people are to entrenched in their duties and day to day operations. And neither will IS departments facilitate or project this into a strategic objective. Building the infrastructure does not require the skills necessarily of IT people. In fact, they may not be able to easily make the mental shift from the client/server or network centric database applications they find themselves supporting today, to the methodologies and tools to build SKA(s).

Consequently, it is not very difficult to understand that if you invest in the development of knowledge capital, business owners and shareholders expect a return or an increase in the value of their stock. Framing the strategic objective, the future role as it is an integral part of CPM for optimum performance through better precision in the decision process streams, the foundations for decision process improvement will be laid down with linked data infrastructures. The infrastructure development needs the financial model that makes knowledge capital possible. Optimum results at the CPM level in real numbers and better decisions that translate in to the goods and services for customers is the realized return on investment shareholders come to expect.

In review, it can be postulated that having the know how to rewire the enterprise with linked data infrastructures and cutting the clothe of the business schema’s representative of ontologies is not enough to close the sale here. After all, most companies barely appreciate information resources management because managing information as a resource is very much immature. The linkage to make this happen must be when expenses to acquire information becomes the means for gaining Knowledge Capital. Managing information as most people accept it today, must shift to being an asset management perspective. As soon as the new financial perspective dawns, Semantic Knowledge Assets will follow as the strategic objective deploys.

Illustrative Cases

A short case in point will follow. Even though people like the view from the top, you have to go where the rubber meets the road. A linked data enterprise will probably take its seeds from existing operational systems. Assuming the virtual servers that host semantic technologies are in place, lets take a look at a decision process before and after a Semantic Knowledge Asset is developed. Two fictitious examples follow to illustrate Semantic Knowledge Assets in action. The first case is more exemplary of a sales and marketing environment. The second example may reside in a regulated manufacturing setting having to do with a filter product.

Commercial Operations – Sales & Marketing

In almost every large business today the enterprise will use one or several mega-software application such as Enterprise Resource Planning (ERP) or Materials Resource Planning (MRP). In Sales & Marketing the Customer Relationship Management or (CRM) is the standard application available from many large vendors as well as some absolutely amazing Open Source Software products like SugarCRM⁷. I have to believe that in the very near future, the face of these separate systems will change drastically. With a linked data infrastructure, think of this in terms of connecting your sales database to your financial systems with your operations database. Instead of giving you this in technical terminology, it is easier to imagine what type of knowledge can be mined from this combination. It is as simple as converting raw data from your CRM database with other applications as mentioned above, into a Resource Description Framework (RDF) known as a triple. Connecting to your business schemas or ontologies if you will, changes the entire competitive picture for the Marketing department. The representation of fundamental concepts such as product lines, market segments, business rules and even perhaps competitive intelligence will allow new insight for making business decisions effecting the revenue for the firm.

This type of Semantic Knowledge Asset is known as the Enterprise Semantic Web. It is internal use only even though some data resources may come from outside the firm. This is much different than data mining terabytes of information and reprocessing it. The result are solutions that let your associates change the business rules related to product classifications, product lines, and product hierarchies against market segments. A common problem that may be handled by spreadsheets and prone to errors is to show how sales results fall into different market segment buckets. With the SKA this is consummated through runtime inference engines because now that data is associated with meaning (i.e., linked). The automated reclassification and total visibility comes as knowledge realization when financial analysts have the ability to run and test different reporting scenarios and instantly see how it impacts the bottom line. This type of scenario was achieved in the past by custom programming, add-ons or retrofit software to many of present day enterprise applications.

SKAs have greater implications for supply chain logistics and exactly where you might best project your sales force. If your firm is marketing 2,500 different products, with the SKAs in place, it will be a much less difficult task to weed out the good and the bad revenue generators and analyze margins. Why you ask? Because presently the type of data most commonly manipulated is known as homogeneous data. Homogeneous means that the data maybe composed of similar or common elements or parts and has a certain uniformity. The type of data most often under utilized is heterogeneous. Heterogeneous means the data is composed of unlike or unrelated elements complex in structure, consequently the tools in common use for homogeneous data will not work as well. A component part of SKA development is how to take advantage and visualize highly interconnected and typed data. An excellent example of this is a tool from MIT called EXHIBIT. These tools allow the user to develop the perspectives that make the most sense.

Decision Under The Old

In many modern production environments, things can and do go wrong. In regulated industries such as banking, food processing, nuclear energy or pharmaceutical operations, the things that go wrong are known as incidents or non-conformances. Typically, something that is non-conforming must be investigated for two reasons. The first reason is a regulating agency may require the firm to do so by law or license. The second reason is if you want to improve your process, product and quality, you must investigate and evaluate your non-conformances. Six Sigma initiatives to eliminate defects may even be applied as well as LEAN methodologies to maximize process velocity to this end.

There are several names that are congruent for describing the same thing. In Security Management Consulting, workplace violence, Intellectual Property Theft or counterfeit products to name a few are investigated as Incident Reports and usually handled by the Security Director in a firm. In a manufacturing setting defects might be recognized as deviations. In marketing departments, competitive intelligence reports and comparative analysis are used to direct and maintain market share. Whatever you wish to call it, the subject in question probably has something to do as a non-conformance issue.

The US Food and Drug Administration (FDA) is responsible for the Code of Federal Regulations called Current Good Manufacturing Practice⁸ (cGMP). Manufacturers must comply with these regulations. If a drug manufacturing process fails to comply with any part of the regulation, it must be reported. A completed incident or deviation report is rich in knowledge. It consists of the investigation, evaluation, root cause, corrective action, preventative action and a product impact and risk analysis statement. In most cases, these are reviewed by many people before final closure. Incidents are expensive from start to finish. They may consist of such things as operator error, a procedural process error, equipment failures, environmental monitoring, out of specification and out of tolerance just to name a few broad categories. The same holds true for OSHA compliance to Federal Regulations within a firm. These are two example areas in a regulated environment. So to be generic, we will refer to all of these as non-conformance reports.

Non-conformance reports are like any other corporate record, they must be preserved with all the associated supporting documentation for many years in the case where auditors, compliance officials or government agencies need to go back and research product safety issues related to a particular product line or batch. One that comes to everyone’s mind is the recall on children’s toys manufactured overseas containing lead in the paint. A company’s repository of non-conformance reports is a vast store house of knowledge locked away, literally. Software applications are used to track quality and the standard implementation is a User Interface front-end for editing and managing the work flow of reports supported by a relational database that is usually tightly controlled for security reasons. Consequently, these issues present a blockage which will require support from an authorized strategic objective to help build the knowledge asset.

The life cycle of a report does not lend itself to automation very well. It is intensely laborious work. The work is an excellent way to become the subject matter expert in what ever business you find yourself in. It brings together many subject matter experts. In processes that are complex, the investigator is required to pull data sometimes from dozens of repositories or silos such as databases, content management systems, including paper records and interviews with personnel.

After the non-conformance report write up is completed, the report usually goes through various comprehensive review processes for correctness, readability, completeness and consensus with a quality assurance compliance group or official that finally closes the report out. Needless to say, many decision streams are involved starting with the principle investigators skills in researching, investigating and composing the report. Reviews by middle and upper management as well as compliance officials must also decide to return it for more investigation, clarification or rewrite. Once again decisions are being made throughout the life cycle. More often than not, reviewers have questions that are pending before their decision to move the non-conformance along to the next level of review.

A very casual question from a reviewer might take hours to answer. For example, an evaluation states that potable water used in a piece of equipment uses a 0.354um filter medium. The reviewer seems to think that it should be 0.3um and inquires with the investigator to confirm the statement in the report write up. The investigator relied upon production technicians for the information because they are responsible for changing out the filters. However, in this case to satisfy the reviewer, the investigator has to locate the filter in inventory, collect the specifications from the supplier or manufacturer and retrieve an official equipment validation document in order to assure the reviewer that in fact it was a 0.354 um filter. Anyone in the LEAN world will tell you that if it takes more than 30 seconds to locate a paper document in your office with the information you are seeking, it is probably taking to long and time is wasted. This is one reason non-conformances are costly. The wasted time in locating information stalls the decision process which highlights a practical area for improvement.

Decisions Under The New

In light of the casual question asked above, lets look at the scenario from a Semantic Knowledge Asset perspective. One reason at least for this example, for building this out as a SKA is so that questions can be asked that are prerequisite to making a decision in that it does not take all day to get an answer. Developing the asset means that within the knowledge domain for which we are tracking and writing non-conformance reports, the linked data infrastructure would consist of an integrated platform perhaps utilizing the Virtuoso Universal Server by OpenLink Software⁹. This provisions the basic features of the Http protocol access to or exposure of data, universal resource identifiers (URIs) and the data interconnections, interrelationships and context useful to humans. From the domain expertise and subject matter experts in the department where the non-conformance occurred, the core knowledge asset is constructed.

The core asset utilizes the tools and integrated technologies of a semantic web. With the recent release of the Upper Mapping and Binding Exchange Layer ⁸ (UMBEL) and its linkages to existing ontologies, the day has finally arrived when integrated access to all data brings real value for improving the decision process. For example, data residing in spreadsheets can be converted to RDF. The legacy data that is bound up in your quality assurance application for managing non-conformances can be expressed as linked data. The unstructured validation document stuck in an electronic Document Management System can now be exposed as well as the manufacturers filter specification digitally bound in an AdobeC pdf file. Linked data can be applied to any formalism, source or schema. It is perfectly suited to integrating data between disparate systems whether that data is "Open", private, proprietary or subscription based.

In the best spirit of interoperability, linked data can be shared or exchanged between two or more enterprises, private parties or departments over a private network or intranet using Http. This is a great flexibility because an inquiry by a person reviewing the report may not have to go back to the investigator for a quick answer. And for an investigator, a good percentage of his time spent before writing the first sentence of a report is in locating the data associated with the component parts of the report. The boundaries where the data islands exist in the enterprise begin to fade with this new capability to access information unconstrained in a coherent and contextual sense and a certain level of confidence.

The non-conformance report, no matter what the category can be abstracted as having many subject concepts. The UMBEL is a repository of 21,000 subject concepts , a broad reference structure that can be accompanied by more specific domain conceptual ontologies to provide focused domain-specific context. In reading the report, the word "filter" may be a subject concept. The filter may not be particularly well identified because its use within the described system is implied. But essentially, the word filter as a Node in UMBEL would resolve to one or more domain specific ontologies and perhaps a selection of named entities. In this example, the named entity was a brand name product. This class of equipment uses a filter described as "Class P Grade Encapulation". We are not so much interested in the filter housing per se as we are in the actual class of the filter. The capsule as a class has specific properties. When these properties are structured data in our SKA taken from the manufacturers technical specifications, we now have the ability to find out (i.e., electronically search) the operating characteristics of the filter. The reliability in terms of accuracy comes from the comprehensive validation testing by the manufacturer to ensure consistent and reliable performance under a range of process conditions and the properties declared by the manufacturer are not ambiguous.

In Knowledge We Must Trust

In the event that we actually improve the decision process, the knowledge from inquiry must be trusted in terms of a level of confidence in its validity and accuracy or precision. The decision made may only be as good as the degree of confidence we have in the knowledge returned. Thus, when building ontologies, the larger concern within a knowledge domain is that of ambiguity. That is to say, a term may have more than one meaning or a slightly comparable dual definition depending on your world view of things. This is often a conflict between subject matter experts when developing ontologies. Building the SKA should go through a process of validation not dissimilar to methods used in the pharmaceutical industry to confirm that a system is going to work the way its specification and design says it is going to work. In pharmaceutical firms, buildings, equipment, processes, computer systems, etc., all go through validation before government agencies will license them for use in producing product. To qualify these systems, three testing methodologies are used IQ, OQ and PQ; Installation Qualification, Operational Qualification and Performance Qualification, respectively. While the strategy of validation will undoubtedly be different for SKA development and deployment, it should be no less rigorous regardless of whether or not the area of domain must be licensed if we expect our decision process improvement to yield confidence in the intelligence the SKA is able to provide.

A feature that an SKA can provide for is cross-reference to separate linked data endpoints. If the person reviewing the non-conformance report, queries about the capability of the filter, multiple sources may result.

1.       The query may return the value from the qualification protocols that were run during validation. This tells us what filter we should be using in the water system equipment.

2.       A query may return information from our materials inventory control system located in a materials management system (MRP) application with the commodity number ensuring that technicians are replacing the filter with like-for-like components.

3.       It will very likely return the micron value as given in the manufacturer’s specification.

4.       And, if our SKA is really robust, the query might return information from the equipment preventive maintenance schedule showing when and what filter was replaced.

The knowledge coming out of the SKA confirms the use of the 0.354 micron filter. The confidence level is high enough whereby the reviewer decides not to ask the investigator to verify the statement in the report. In Appendix I is a graphic depicting a SKA in bioinformatics from the Quebec Genomics Center on the Semantic Web Atlas of postgenomic knowledge about the human and the mouse. It serves to illustrate in a bioinformatics domain the elements in an SKA and the distributed nature of knowledge from many resources.

Fewer Mistakes

People make mistakes or sometimes might change the truth about facts or statistics. Most of the time, non-conformance reports will have honest mistakes and if the firm is lucky, the mistakes will be recognized by the reviewers as the report goes through the review process. Mistakes are also costly in more ways than one. For instance, a formula used to support a product impact and risk analysis statement if computed incorrectly may lead to a wrong decision regarding the safety of the product resulting in the company discarding it, meaning it does not go to market. Or worse yet, a product was judged to be safe when in fact it was not and it was released by quality assurance for use in further manufacturing processes and ultimately making it to the marketplace. Granted, there is no fail safe system when it comes to human error, however relying upon our knowledge asset for verification purposes may help people decide when it is appropriate to question a set of facts (i.e., re-validate statements made in non-conformance reports) and join in consensus upon the truth of the report.

A decision made by way of a mistake is going to impact CPM. The fact that a single query sourced out four distinct references about a filter in the context of its use is not to supplant our own reasoning, rather its aggregation gives us some inference power for which the reviewer did not have when first encountering the question about the size of the filter in use. All improvements made to the decision process whether that includes the acquisition of better knowledge or reducing the number of decisions will impact CPM in a positive way.

Mining The Knowledge

While this discussion is not a specification for developing the Semantic Knowledge Assets, there are several ways to harvest the knowledge. The query language for linked data is called SPARQL (pronounce "Sparkle"). SPARQL is only applicable to an RDF data graph. Dataviewer applications, semantic web browsers and other tools facilitate the presentation layer. Writing non-conformance reports is not an automated task. However, templated SPARQL queries and other techniques can lead to very efficient and rapid deployment of various Web services and reports allowing investigators semi-automation in gathering information. For example, if the database application for managing non-conformance data was integrated into the SKA, trending across all past or closed deviations would be significantly faster and accurate. Binding instance data with display templates such as Fresnel with a SPARQL template would yield efficiencies as well. From the bioinformatics example in Appendix I where data converted to RDF was aggregated from available public resources, from the 65 million nodes graph you can ask a real question: "What characteristics of protein were assigned to genes involved in Paget disease?

Semantic Web Browsers allow for faceted browsing over a collection of structured data assets. This is unlike standard web browsers as these applications let you see information in a different light. Faceted means filtered by type or category such that you get to the information that matters much faster visually instead of by scripting. A faceted browsing example for a commercial real estate firm might be in aggregating real estate listings into a Semantic Bank and is useful for facilities management of real estate assets:

Advanced applications like the bioinformatics Cytoscape has blazed the trail in the life sciences R&D areas. Although Cytoscape was originally designed for biological research, now it is a general platform for complex network analysis and visualization. In Appendix II, Cytoscape generates a visual that is a knowledge map derived from the SKA production in Appendix I.

Difficult Decisions

Most all decision making is not as simple as asking a question. Reasonably difficult decisions involve many variables in time and space. The SKA has very little to do with the complexity of difficult decisions. Using the SKA aids us in pulling together many facts related to the decision problem. In fact, knowledge may be drawn from across several domains. Aggregating the information coming out of our SKAs allows us to combine things into potential actionable intelligence to apply to the problem space of a decision. The databasing of RDF is known as a Semantic Bank¹¹.Consequently, as you gather knowledge that addresses the multiple variables leading up to a decision, you can store, publish, exchange, browse and manipulate your findings as a collection in the Semantic Bank.

In the security consulting business, this type of aggregation is called intelligence gathering. So no matter whether you are investigating a non-conformance or a crime, the principles are the same. Information of varied importance is retained. Clues start to bring things together and logical conclusions let the investigator build a case. The volume of related knowledge to a case can grow very quickly. Organizing the facts derived from Semantic Knowledge Assets is in itself a knowledge asset that may guide one toward different lines of inquiry. Ultimately, your decision may be to prosecute the crime.

The linked data infrastructure remains the same no matter how many SKAs are developed. After a large multitude of SKAs are in place these assets need to be managed. A Semantic Bank may be a good tool to enumerate or index the assets for corporate wide exposure. And the efficient management of knowledge assets has everything to do with its residual value and reuse, an important component in the valuation of a firms Knowledge Capital.

While the linked data infrastructure facilitates structured, semi-structured and unstructured information, much of the discussion is on leveraging legacy data and business schema’s. However, since many firms today are creating knowledge through their research and development organizations, it seems like parlaying this directly into a SKA is most cost effective early in the R&D process. In fact, some pharmaceutical companies are already realizing that structuring data may enable them to bring new product to market faster. This has implications for everything from research and development, to manufacturing, to pharmacovigilance and to sales and marketing. The shear volume of information in developing a new product often is the culprit for not meeting milestones or requiring additional labor costs. With such large amounts of information comes less understanding. With semantic enhanced data your chances of managing the resource is vastly improved.

The Foundations for Decision Process Improvement has touched upon the following key points:

• Better support for alternative decision paths.
• Improvement upon decision streams as laminar flows.
• Linked data infrastructure as a key element to improved decision processes.
• Semantic Knowledge Assets built upon the linked data infrastructure to yield intelligence.
• Valuation of SKAs as investments in Knowledge Capital yields value to a customer via optimal decisions.
• Finding information faster to prevent the decision process from stalling.
• Confidence in the intelligence queried from multiple linked data endpoints.
• Trust in the precision and accuracy of the intelligence from vetted structured resources, vocabularies, ontologies, etc.
• The knowledge asset as a means of verification on subject concepts.
• Multiple sources returned from the SKA and cross-reference may lead to reducing the number of decisions in the stream.
• Presentation not currently available for unstructured data yields new insight in the way we look at decision problems.
• Aggregation of knowledge artifacts necessary for analysis leading to a decision.

Closing Remarks

The most amazing and remarkable thought coming out of this discussion is how absolutely easy it is to deploy the linked data infrastructure in the enterprise. The decision maker with Semantic Web applications can now interact with data at the conceptual level as opposed to the traditional logical level. Data about customers, suppliers, invoices, and orders, stored in almost any existing database can be referenced and presented in RDF form across database instance, machine, and network boundaries. This will afford your company a leadership position via an Innovative and Pragmatic use of Semantic Web Technology. Successfully achieving your initiative toward Decision Process Improvements across the enterprise, the Semantic Knowledge Assets facilitates powerful holistic views and traversal of enterprise data relationships whereby making great decisions for Corporate Performance Management ensures Key Performance Indicators promote the growth, resiliency and continuity of your business.

It has been said that "You cannot not manage what you cannot measure", implying the use of metrics is an absolute. Thus, when a strategic objective has been cast, the formulation of the strategy has a clear set of objectives for execution. Critical success factors are then linked to well chosen performance metrics, so-called key performance indicators. CPM is in union with or intersects with DPI. If operational performance was perfect, there would be no need for decisions to make adjustments by management. In fact, metrics alone does not guarantee optimal decisions. The decision maker also may not have the perfect expertise and skills through education or experience to make the best choice. Supplemental to metrics are two precepts derived from SKAs for DPI. Knowledge acquisition involves on the part of the decision maker complex cognitive processes: perception, learning, communication, association and reasoning. Knowledge is also used to mean the confident understanding of the subject with the ability to use it for a specific purpose. Identifying a good balance of critical success factors should also include the yield from incorporating the use of knowledge assets. A deeper meaning to the metrics and concepts, highly relevant but not necessarily more facts and clarity of the bigger picture will lead to better decisions and less variation over the course and real-time stratagem execution.

Q. Fear! By making my data machine-readable, will it lose human comprehensibility?

A. No! It is falsely assumed that if one gains the semantics, you lose the visual understanding. Visual understanding is why so many people like Web pages. Thankfully, there are many ways to see data and work with it. If semantics help to describe the data, then your viewing options become more flexible and compelling.

Q. My company has been in business for 100 years. How in the world do you suggest we go about this?

A.  I would suggest engaging people with the expertise and vision in this technology area to gain an orientation. The approach will vary depending upon the dynamics and evolutionary processes your company goes through over time, (i.e., such things as product life cycles, economic expansion or contraction in markets, etc.). Mapping your knowledge domains and potential knowledge assets in context with estimating valuations. In practical terms you would not invest in linked data infrastructures if you are going to sell off one of your business units or sunset a product line. On the other hand, if you are conducting R&D or product development, building the infrastructure will make a better financial case because the codified knowledge has reuse value over an entire product life cycle.

Q. How about my current IT investments?

A.  Most IT investments today support their user community with proprietary desktop operating systems and applications. This means you are locked in and not easily adaptable to change nor does it let you improvise to meet new challenges in business. Realize, the semantic web and the discussions about linked data has largely come about with the help of Open Source Software and open standards as bio2rdf.org is a prime example, or Protege for developing ontologies. Consequently, you have to be open minded to the same extend that I am suggesting you to have an open attitude with regard to the valuation in Knowledge Capital. Fortunately, most Open Source Software can run on most of the operating platforms available today. Thus, it is a matter of better leveraging your current IT investment and perhaps doing away with expensive legacy applications as you deploy SKA technologies.

Q. Where are the skilled people in my organization going to come from?

A.  In all likelihood, your people are already subject matter experts within the knowledge domains they work in. This question may be more a matter of whether you empower your folks to innovate and integrate. Not all SKA development can be born from one mold. Once you map and design the knowledge asset, it may not be so much a matter of programming skills as it is a task of integration of existing tools. And in so many companies, people are stifled from being creative which is exactly what you need in order to be successful in building the SKA.

Q. You said this was going to be easy, really?

A. I said it was going to be easy. I did not say it would not take a great deal of work. However, I think it will take less time to do than what companies go through today, lets say to bring in outside software applications into a regulated environment which requires extensive validation. By the time all the red tape is done (5 – 6 years) and the associated risk you have deal with, such as a critical component of the system like a server no longer supported by the vendor, the whole thing may have to start over before version 1.0 of the solution was ever deployed. I still think comparatively speaking it is easy given the Open Source availability of tools and companies like OpenLink Software servers.

Glossary of Terms and Acronyms

CPM - Corporate Performance Management

faceted browsing – ‘faceted navigation’ gives the users the ability to find items based on more than one dimension, to see breakdowns and projections of the items along different axis, which helps users gather insights about the data they are exploring. The most useful ‘faceted navigation’ is ‘context dependent’, meaning that available facets, facet values and their count is based on the current set of results that the user is browsing.

Fresnel - is a vocabulary for displaying RDF. A Fresnel was also a type of lens invented by the French physicist Augustin-Jean Fresnel originally developed for lighthouses.

IEEE - Institute of Electrical and Electronics Engineers

LEAN - is the optimal way of producing goods through the removal of waste and implementing flow, as opposed to batch and queue.

SPARQL – A query language for linked data

UMBEL - Upper Mapping and Binding Exchange Layer – A lightweight, subject concept reference structure for the Web

URI - uniform resource identifiers

Zitgist – as in: zeit•geist \ tsīt ‘ gīst \ n. [ German fr. zeit time+ geist spirit ]. The spirit of the time; the taste and outlook that is characteristic of a period or generation.

as in: gist \ jĭst \ n. The central idea; the main or essential part of a matter.

This diagram is used with permission by Francois Belleau and Marc-Alexandre Nolin and is taken from a research paper by Francois Belleau, Marc-Alxandre Nolin, Nicole Tourigny, Philippe Rigault and Jean Morissette titled "Bio2rfd: Towards a Mashup to Build Bioinformatics Knowledge System".¹²

This graphic image is used with permission by Francois Belleau and Marc-Alexandre Nolin and is taken from the slideshare presentation by Francois Belleau, Marc-Alexandre Nolin and Philippe Rigault given at the ISMB2008 Technology Track titled "How to use Bio2RDF with a Virtuoso server.

¹ Strassmann, Paul A., 1998. The Value of Knowledge Capital[online]. Strassmann, Inc.

Available at: http://www.strassmann.com/pubs/valuekc/ [accessed 01 March 1998].

² Institute of Electrical and Electronics Engineers. IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries. New York, NY: 1990.

³ Berners-Lee^, Tim, 2006. Linked Data [online]. W3C World Wide Web Consortium.

Available at: http://www.w3.org/DesignIssues/LinkedData.html [accessed 02 May 2007].

⁴ Zitgist, LLC. 2007/2008. Definition of Linked Data [online].

⁵ Bergman, Michael k., 2008. AI³ Adaptive Information, Adaptive Innovation, Adaptive Infrastructure [online].

⁶ Strassmann, Paul A., 1998. The Value of Knowledge Capital[online]. Strassmann, Inc.

Available at: http://www.strassmann.com/pubs/valuekc/ [accessed 01 March 1998].

⁷ SugarCRM, Inc., 2008. SugarCRM Commercial Open Source [online].

⁸ Center for Drug Evaluation And Research, 2008. Current Good Manufacturing Practice (CGMP) Regulations [online]. US Food and Drug Administration. Available at: http://www.fda.gov/cder/dmpq/ [accessed 04 Aug 2008].

⁹ OpenLink Software, 2008. Virtuoso Universal Server [online].

¹⁰ UMBEL.org, 2008. Upper Mapping and Binding Exchange Layer [online]. UMBEL.org.

¹¹ Massachusetts Institute of Technology, 2008. Semantic Bank [online]. Semantic Interoperability of Metadata and Information in unLike Environments (SIMILE).

Available at: http://simile.mit.edu/wiki/Semantic_Bank [accessed 04 Aug 2008].

¹² Belleau, F.; Nolin, M.; Tourigny, N.; Rigault, P. & Morissette, J., "Bio2rfd: Towards a Mashup to Build Bioinformatics Knowledge System", Bio2rdf.org. Available at: http://www.ncbi.nlm.nih.gov/pubmed/18472304

Decision Process Improvement (DPI) is an idea that I think goes hand in hand with the development of linked data. The very realities of the Semantic Web, the feasibility of totally integrated data across the enterprise must necessarily be a paradigm shift for the way decisions are made in business. The assembly of knowledge assets and the business intelligence that will be derived will cause us to rethink and improve those processes that lead to how we make many decisions throughout a business day.

Decision Support Systems (DSS) have been around a long time, as have data warehousing & mining, OLAP, executive decision support systems and enterprise resource planning applications to name a few to help us get through a day in business. DSSs are a class of information systems that in many ways are processes toward decision making. And, if there is any universally accepted taxonomy of a DSS, I would like to hear about it. The basic classifications are passive, active and cooperative systems. Then, there are the different modes of assistance that are required:

1. A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model.

2. A communication-driven DSS supports more than one person working on a shared task.

3. A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data.

4. A document-driven DSS manages, retrieves, and manipulates unstructured information in a variety of electronic formats.

5. A knowledge-driven DSS provides specialized problem-solving expertise stored as facts, rules, procedures, or in similar structures.

Other means of supporting decision processes include reports, dashboards and scorecards.

Now a new factor comes into play, that of structured data. All the previous aforementioned support systems pull information resources from distributed repositories or silos. The trouble though is no business has ever achieved the holy grail of IS and that is a complete integration of data across the enterprise in terms of interoperability. Consequently, while each has its purpose they all have drawbacks as a result. Most of the data lacks structure and semantics.

As more firms take initiatives to structure their data internally, machine to machine processing and search for knowledge across this integration will be offering up this shift putting pressure on the way we think about DSS. This thought process is Decision Process Improvement in the form of new tools that may cause the marriage between some of the existing assistant models.

8:00 AM – Business Intelligence Brief

The Daily Intelligence Brief sounds more like a CIA or FBI confidential document prepared for the President of the United States or Secretary of Defense on the state of our nations security. The “Right Hand Man” for a busy executive or owner is the John Wubbel Consultancy’s Business Intelligence Brief. Executive Briefs are abbreviated summaries that sift out extraneous information and provides the reader with the most pressing details on a particular topic. The 8:00 AM Business Intelligence Brief (BIB) differs from Executive Briefs that read like news in that a BIB is actionable intelligence on a cross section of events, issues or strategic initiatives that may impact business operations. Consequently, it empowers the executive decision maker with a criticality factor and linked data to the originating source if more in depth details are immediately required. A criticality value in context gives the sense of priority, potential risk, or urgency while the action part suggests or infers which direction a decision should take. And this ability to make a more precise decision is the pay off in terms of productivity, operational resiliency and continuity.

The underlying mechanism is a proprietary platform with an automation engine that supports BIB push, pull or emergency notifications delivered to exactly the right executive or senior management person in a position to act upon the intelligence. This is crucial in realizing the savings BIB can bring to an organization.

Decision Process Improvement (DPI) is greatly enhanced through the automation capability to compensate for each reported item over the life cycle of an event from day to day BIB deliveries. The factors of the key items reported in BIB are continually reanalyzed for items that may go hot requiring immediate awareness by management for minimizing impact on operations. Otherwise, they are updated in the next day 8:00 AM BIB release reflected by the criticality value and its priority relevance.

The intelligence brief is not just for top executives. A wide variety of applications are finding practical use for BIB in business process improvements as for example between different departments or organizations within a firm. As a knowledge communications device, one department might maintain dynamic information yet, another is solely dependent upon that intelligence to execute its mission. The BIB can take Intel from the many and report direct to the few eliminating shotgun distributions. Everyone from Deputy Directors, Directors, Senior Directors & Management staff to Vice President levels become proactive in their operation management activities.

Gosh! If you made it this far in the 3 part series, you deserve a tip.

What do you carry around with you all day that is so valuable and you never have enough of it?

Answer: Your Attention.

Your attention is valuable because things, people, or places require your attention. You constantly pay into it, or things, with your attention and because it is a scarce resource it is precious. And, so as not to waste it, the focus on knowledge, its acquisition and quick assimilation is critical to your success. Knowledge realization will optimize or aid you in making the best use of this finite resource called “Attention”.

From Part I of this series, “How do you use knowledge as opposed to indiscriminately discarding it as an asset?” Well again, I have to come back to the issue of costing it out. If you have been in your profession for ten years, you are probably earning 3 or 4 times what you earned when first starting out. So how do you justify the difference? It can be explained by the accumulation of company specific knowledge or if you are in business for yourself, everything you learned by being in business. Investing in that learning experience to become more functional and effective does not show up in direct costs. Everything from company gossip, meetings, phone calls, conferences and yes errors made and corrected.

I know, you are going to call this “company overhead”. Not something customers are willing to pay for. There is another way of looking at this though. Call it the money spent on the accumulation of company specific knowledge capital. I doubt human resource departments even think of it in these terms. But if your ten years of experience is anything, including periodic investments in your training, you are worth more than you are being paid. Since this is the case, your company or the company you work for can recover its investment in knowledge capital as incremental profits. The only way this may fail is if you took training that is not relevant to your job or the skills being taught and practiced are obsolete. No value is added in these cases.

Given the nature of the Web, Open Systems Software and the structuring of data in a virtual workspace, the amount of knowledge coming on-line and the accumulation at a faster and faster rate, only serves to reinforce the notion of leveraging this capital. Indiscriminately throwing away these assets can be as simple as discarding software or deleting a spreadsheet. Another way, and I have seen this happen so many times is the loss of an employee. When the employee leaves the enterprise, so goes some capital. Of all the ways we can lose or discard knowledge assets, much of it resides in digital form totally unstructured, sometimes difficult to find and most of the time since it is digital you have knowledge lockout. You have to open the document and read it. It cannot be queried for knowledge or searched contextually. You want to have your business intelligence at your finger tips otherwise it is going to take up some of your attention. Before you can use knowledge you have to be able to get at it. And before you can get at it, knowledge must be accumulated.

Software is a significant store of a corporations knowledge capital. However, you must recognize first, the fundamental change in how the masses view the term software. Even fairly sophisticated business people have a consumer grade orientation. You have the basic MS Windows OS with your office suite, email and a couple of specialty applications. We have been sold on the idea that we are managing information. When the OS changes, which never co-incidents with changes in technology, your organization or business practices, you end up dumping the old version, wasted software expense, converting your old documents and data to buy the new. That is the discarding of an asset containing perhaps unrecoverable knowledge.

The fundamental change that has been occurring is that data, used by new software application paradigms (Open Source Software built to Open Standards), is becoming structured and software being constructed today must have a high residual value (i.e., reusable) and be a low maintenance capital asset. So what you really need to do, is evaluate the size and quality of your software portfolio. If you have not given any thought to this, chances are you are under utilizing all those millions of lines of code. A software portfolio will give you the means to think about it as an asset that can be managed. And this is the opportunity for you to accelerate the accumulation of knowledge capital. Knowledge capital is the encapsulation of accumulated expert knowledge capable of preserving an enormous amount of information. The knowledge asset should be managed so that it keeps growing steadily, safely and reliably.

As long as software is viewed as a expense that must realize short-term returns, corporations will be paying over and over for similar business function without the benefit of reuse. You will never make that shift from what we presently call management of information, viewed as technical efficiency, to an asset management perspective. The expense of acquiring information capabilities must change to become a means for gaining knowledge capital.

You will not be able to make this transition until your mind set changes. And the reason most people cannot realize there is a transition to be made is there is no easy way to make information and knowledge an explicit measure of performance. It does seem intuitive that once we know our knowledge assets, we can now build strategies, utilize tools and decision support systems to use and put that knowledge to work.

In closing, when I am talking about converting your real estate data into a structured (knowledge representative) form, i.e., exposed PropertyBanks in an RDF file, (see my activerain.com blog on PropertyBanks) this is only one area or component of the big KM picture. However, accumulating knowledge is like compound interest. Its organization and ease of access returns incremental performance when you are more competitive, quicker with the complex solutions for customers, can give the best customer service and operate more efficiently. Knowledge Capital does not solely exist in the minds of employees. It may also occupy a mind-share in customers who have spend their time and money to learn about your products and services.  I hope you have enjoyed this perspective series.

Before going further with the discussion about the value of knowledge, it is helpful to identify the knowledge you might already have in your business. When you open the office in the morning and make the coffee, you may brew a particular brand or flavor that you know is favored by many people in your community. While the coffee pot is going, you turn on your computer or pick up the morning paper. The knowledge you have in your office exists in many forms such as books, papers, mathematical formulas, procedure manuals, state and federal regulations or guidelines, computer code, decision trees, and so on. We often do not inventory these assets. This knowledge is codified, in the form of accumulated facts, methods, principles, or techniques specific to your line of business. The general term for this type of knowledge is called explicit knowledge and its primary attribute is it can be shared.

Each and every one of us is unique, a knowledge repository often called a subject matter expert or that which is in your mind, a domain expert and hence only usable by you the expert in your line of business. This second classification of knowledge in called implicit knowledge. At this stage you may be thinking it is easy to calculate the value of your expertise. Adding up your college education, real estate training courses, seminars and conferences over the years. Everyone has done this at one time or another. I might pose to you that the real difficult component of valuing your knowledge is the constant efforts you make on a day to day basis in doing your job better and better beyond your college days, learning from mistakes and keeping certain things you discovered that work well a secret. Your expertise does not shine until perhaps you have served a customer well or achieved a milestone.

Huge egos aside for a moment, why are 2 managers better than 1? Or, a whole office full of managers at different levels better than any single professional? What I am getting at here is that as a business entity you cannot think in terms of knowledge capital until you consider the organization and enterprise as a whole. This is the accumulation of company specific knowledge as aggregated in people. An even more difficult notion to valuate. However, you may have noticed when this really works. For example, when you have participated in a really successful meeting where the agenda was accomplished and all the team members brought their think power to solve problems or plan strategy. Each persons years of accumulated knowledge, the investment your company has made in training its employees is worth more than what the company pays in salaries. Successful meetings internally or with customers and executing on plans is where the enterprise can realize incremental profits on the firms knowledge capital. The enterprise must manage knowledge capital as their most significant asset.

To know something is to “know about it” leveraging a certain capacity for action which corresponds to “know how”.

In Part III, the post will have a few follow up thoughts and then discuss ways to use this asset.

1. How does one put a value on the value of knowledge?
2. What do you call it after you recognize that knowledge in your enterprise has value?
3. And how do you use knowledge as opposed to indiscriminately discarding it as an asset?

You probably have a good inventory of knowledge assets even though it is titled by various subtle names or categories. But in the aggregate you can call it Knowledge Capital. Putting a value on it is tricky. Traditional accounting principles or methods may not quantify it due to the fact that so much of it is intangible. However, in most business mind sets over the last 100 years, value was only thought of in terms of capital and labor. Capital assets increased the productivity of labor. So in accounting terms the productivity of the business could be measure in terms of how much productivity did the capital generate, such as return on assets or return on investments.

Since my blogging theme has so much to do with knowledge and structuring unstructured information into soft assets to facilitate your knowledge retrieval capability, it is a good idea to think about why this transformation of the Web is in your future. Contemplating the value of knowledge is the only worth while endeavor to come to grips with it.  As a business person, you probably spend more time and labor over developing and executing upon your knowledge.

You may not actually provide capital to a project unless you are a commercial real estate developer. In the traditional thinking the persons that provide capital share in its excess or surplus called profit or rent. The only possible way labor can get an increase in wage is if the capital requires some expertise or knowledge. But, those performing the labor are not entitled to rent on the knowledge they have accumulated. Labor is only paid for the time worked and may receive a slight increase or bonus from time to time.

In putting a value on knowledge and in answer to that question, in todays information age going forward to a knowledge economy, the above reasoning for the basis of valuing employees is misleading because it does not take into consideration how fast an employee has accumulated useful knowledge. A few years ago the popular business press was touting the “Knowledge Worker”. Are we there yet?

Whether one is an architect of software, office buildings, marketing or manufacturing, it all seems like labor to most of us and thankfully most of us love what we do. So, I might propose that evaluating labor is not simply that of wages these days. In any company, organization, team or collaborative projects with or for customers, the real productivity comes from knowledge capital. This means that it is the aggregated knowledge in your head from experience, useful training and company relevant experience. From this perspective, you can further evaluate what you do in your enterprise in a new context.

In the next post Part II I will touch upon identifying some knowledge assets you may not have thought about in an effort to answer the 3rd question above.

Linked Data Car Pooling Programs

A practical application must solve a problem. Linked Data and the discovery of its real life benefits will drive it toward maturation. Starting with a domain that almost everyone can relate to may be the starting point of the life cycle  that identifies a problem, engineers a solution and ends with an application or output that people can respond to through the use of models of Linked Data.

Linked data is a term used to describe a method of exposing, sharing and connecting pieces of data on the Semantic Web according to WikiPedia, just to keep this in mind in going forward with this discussion.

All the talk about the Web Of Data and technical jargon that goes along with it is of little use unless people starting finding and building applications that integrate structured data and the hope that researchers and developers have for many of the initiatives coming out of the research. So, we will start with some general observations and try to derive a useful program that many people find productive.

It is very evident that most of our politicians running for office offer great oratory with almost no real solutions to problems. For example, generic statements such as American must use less imported oil. Or, Americans are squeezed economically by the price of gasoline. The country needs to have a summer moratorium on the Federal Gas taxes. We need to put a windfall tax on big oil firms. We need more fuel efficient cars and lower the carbon foot print. And the list goes on and I am very skeptical. Congress is well paid and they fail to agree on much of anything. Well, nothing really happens until Americans have their backs up against the wall. With gas busting over $4.00 a gallon maybe it is time we do something. When I drive South Florida roads and everything is moving at 5 mile per hour or grid locked, I fail to see how adding more fuel efficient cars are going to make gas prices lower. With entire country losing productivity resulting in our being less competitive globally, I rather expect that we need some much larger solutions to the problem. I doubt seriously government is going to take the leadership role in building high speed mass transit mag lev systems to take cars and trucks off the highways.

The solutions are often generalized where the onus is placed upon the public. To generalize though does not necessarily mean the solutions are exercised optimally on a real-time basis once deployed. Examples might be for people to take the bus or train to work. People can buy more fuel efficient cars. If you live close enough to work you can ride your bike. However, lets take car pooling as our problem domain. This may only be an short term solution but it could effect to lower gas prices. Many people participate and certain expressways have HOV lanes to facilitate a quicker trip or avoid congestion in the other lanes. That is a perk for the groups that car pool. Unfortunately, car pooling has its own set of unique problems that tends to discourage utilization or optimization as a solution to decreasing demand for gasoline. In addition, for there to be a real significant difference, car pooling must be done on a grand scale if the country is to realize a savings on imported oil.

While people accept the idea that they may save $20 or $30 a week on gasoline purchases by sharing a ride, the initial problem is implementing a car pool or finding a ride. Current solutions have been tried such as 1-800 telephone numbers for a centralized means of scheduling. Certain companies my offer rented mini-van pooling. Unfortunately, none of these methods empower individuals easily when in fact commuters ultimately make the final decision to participate in a car pool. So, I will propose to use the Semantic Web technology to facilitate a more optimal car pooling solution.

Linked Data alone is not sufficient to consummate a solution. It is a critical component though that may overcome many of the abstract issues that discourage pooling in the first place. For the sake of developing a solution, a hypothetical component mix is presented here.

Scalability

Lets assume that large employers are islands or nodes within the highway network system. As a model that hopefully will be extensible, a short illustration may be helpful. A mythical large employer in Indianola, Iowa located in the corn fields is somewhat isolated. The employer has 3,000 employees, the majority of whom commute anywhere from 30 to 60 miles one way each day. That is an estimated 2,700+ automobiles every day on average making the trip.

Sponsorship

One solution the Federal Government could put forth is to take some of the Federal gas tax money in conjunction with corporate programs to fund Linked Data Car Pooling Programs. Sponsorship will help insure a consistent and compliant level of adoption by commuters. This may be especially more attractive where no alternative transportation such as bus or commuter train are available to a work site location. What most corporations fail to factor into their contingency business planning is the effect that energy prices have on their work force. In the case of Indianola, the company spent a small fortune to expand their parking lot to accommodate 500 more cars. Their car pooling incentive for 2007 was to offer $2.00 lunch vouchers everyday to employees car pooling to work. As gas prices reach $6.00 per gallon, employees are going to start looking for work closer to home. And this may not be good news for the employer.

Linked Data Solution

It is now practical to develop trip data. Many consumers have GPS in their automobiles. Trip data would consist of GPS location data that maps a drivers commute going to and returning from work. Along with the geo-coordinate values would be time stamps with each value pair. Taking this data and RDF’izing it may also be a easy matter with the Garmin RDFizer to RDF script.

The idea of visualizing linked data is the opportunity to discover patterns with greater precision. For example, we may find out that 85% of commuters tend to leave for work at the top of the hour. Knowing such a fact yields the ability to coordinate rendezvous with other commuters in a more timely manner. Through faceted browsing, it may also tell us exactly where the ideal local departure nodes are located. These are the points of convergence or optimal points for loading riders from a given neighborhood when GPS values from the employee’s trip data converge. The reverse is true for trips going from work back to home except the local arrival nodes may differ from the departure nodes. How often do we stop at the local supermarket for milk and bread on the way home? Perhaps the local supermarket parking lot is the final drop off point for a local commuter car pool, a matter of convenience for a group of car pool participants.

Commuters may be required to record 5 to 10 trips to confirm route stability. Random deviations from the most frequently traveled route may happen once in awhile. The most frequently repeated travel route is the most desirable for matching up rides. Establishing the employees most common route and aggregating all employee’s routes will be the raw RDF compilation.

As it stands now, commuters manage  to muddle through the process of finding a group ride that does not make them late or cause them to wait to long for other riders. Casual observation of car pooling efforts find it easy to conclude a short life expectancy for ride sharing unless you have a really dedicated group. So how does this empower the commuter?

Aggregating trip data from the entire Indianola company employee population is key to visualizing it as Linked Data on the web because it would facilitate personal choice. Finding a feasible departure and return trip arrival node via faceted browsing would indicate approximately the number of potential participants on a per node basis while secondarily showing possible departure times. Once again this gives people more choice and increases the likelihood of higher patronization. For the less imaginative minds, route trip data could be a mashup with a Google Map, Mapquest or GARMIN roadmap graphic.

The standard lingo used by technicians and customers of GARMIN units are tracklogs and waypoints. A tracklog is equivalent to my notion of trip data and waypoints for the purposes of our application are the nodes or rendezvous points. The tracklog in a Garmin unit are points automatically stored when your vehicle is moving. It is the electronic equivalent of laying down a “breadcrumb trail” to mark your path. These points will be shown strung together on the map page of your unit so you can see where you have been. Garmin’s definition of waypoints follow:

“Waypoints are locations or landmarks worth recording and storing in your GPS. These are locations you may later want to return to. They may be check points on a route or significant ground features. (e.g., camp, the truck, a fork in a trail, or a favorite fishing spot).” In our case it might be the interchange of 2 routes heading in the direction of the way to work and maybe a significant waypoint for making a connection for a ride.

Fortunately, there has been much work done already to download tracklogs to RDF using python scripts.

1.garmin2rdf.py Reads a Garmin GOPS receiver, dumping the contents in RDF/XML. (Matt Biddulph)
2.fromGarmin.py Downloads GPS data from a Garmin on a serial link to an RDF/N3 file. (SWAP)
3.PyGarmin – A Python Interface to Garmin GPS Equipment available at http://pygarmin.sourceforge.net

And, certain ontologies have already been used such as mapping waypoint symbols to a Wordnet term via the vocabulary namespace. Or, utilizing the Basic Geo (WGS84 lat/long) Vocabulary (reference http://www.w3.org/2003/01/geo) for a basic RDF vocabulary that provides the Semantic Web community with a namespace for representing lat(itude), long(itude) and other information about spatially-located objects as well as our desire to represent trip times. Spatially-located objects would be the nodes or loading points.

The whole point of this exercise is to reduce the uncertainty for commuters and optimize the real-time utilization of car pooling. In addition, where commuters can visualize their options, a Linked Data implementation should promote a self actualization through enhanced communication between riders, existing car pool groups, formation of car pools or groups trying to maintain maximum passenger loads. Exempt employees many times have flex-time in terms of arrival and departure times from work. Non-Exempt employees that must maintain production schedules have strict arrival times depending on what shift is worked. The waypoints or rendezvous locations can be visualized in Urban areas where large number of employees are departing and can be linked by proximity. Knowing the proximity of these waypoints via linked data may improve efficiencies as a consolidated system aggregates potential travel options. Another waypoint might be a stop at a midpoint of a trip to pick up additional riders.

From a sponsorship perspective, agencies, corporations and commuters alike really require knowledge on how big of a financial difference the car pool program is making. Various measures of success may involve metrics related to car capacity utilization or even something as simple as lower vehicle counts in the company parking lot. Companies may actually advertise the Linked Data Car Pool Program as an added benefit when hiring. A sponsoring entity should provision the Linked Data for public access on the Web. As more firms endeavor to deploy the program across the urban landscape, commuters may be able to see new patterns emerge between different company car pool programs or large office parks shared by multiple employers possibly improving the viability for commuters to join in a car pool. This is essentially linking between different data sources for clients/commuters to consume or act upon actionable intelligence derived from the Web. Highway engineers, public administration officials and planners will also find new ways to study and harvest knowledge from the data as a result of the semantics of linked data.

John Wubbel
jwubbel@propertyclubpro.com

In my advocacy of the Semantic Web, I have suggested that you start on the task of making your web site Semantic Friendly by placing your credentials front and center. It pairs up well with the Social Media movement. By RDFizing and structuring your resume, it conforms to what is going on with other entities such as LiveJournal, LinkedIn, etc.

The whole idea behind Social Media and marketing yourself and your business is the fact that you must start or develop a conversation which is that relationship between the business and the customer. Unlike other Internet tasks such as affiliate marketing, search marketing, search engine optimization, usability, email marketing, etc, which has more to do with closing a sale or completing a transaction, one would hope the prerequisite of initiating and cultivating the conversation would lead toward consummation of more transactions.

Make Your Opening now toward a healthier business by getting your credentials linked on the Semantic Web.

In my last blog article, I hopefully encouraged readers to market their CVs and resumes on the Semantic Web. Yes it was a long read, but I think you will realize in time it was worth it.

On May 5, 2007 I wrote the following short article “In Mere Fractions Of A Second” about a Super Fast Semantic Web Search Engine. Sindice is now available for an entirely different type of search.

You may recall that when you have a new URL you could submit your web site to Yahoo or Google. If you are lucky, it may take any where from 1 to 6 months  before their robot will crawl your site to index and rank it.

Sindice is the DERI facility to index the Semantic Web. You will notice the most prominent feature on the home page is their Semantic Search widget. Sindice indexes over 10 billion reusable pieces of information on the web found across 100 million web pages with embedded RDF or Microformats.

Guess What! You can submit your Semantified Resume or any RDF file on your web site and it will index your information resource within 30 minutes for indexing. That is fast. Consequently, I submitted my foaf.rdf and resume.rdf files by submitting them to their interface as a URL:  http://www.propertyclubpro.com/jwubbel/resume.rdf

Very cool. Escalate your Star Power by Marketing yourself, your business or web site on the Semantic Web.

When I took my university courses in computer programming a classic exercise was learning to arrive to a problem solution through a trick called recursion. Example games where this technique was applied are Queens Eight Chess or the Towers of Hanio. (To understand the object of the game see http://en.wikipedia.org/wiki/Eight_queens_puzzle). Essentially you would write code that would follow through a path of moves and at each move test to see if you arrived at a solution. If that move was not a solution, your code would call back into itself repeating to the next square on the board. When it finds a solution, it backs out through the recursion and finds another path to go up. It sustains itself until it finds all the optimal answers such as the 8 positions the Queens must finally land on the board resulting in the perfect solution to the puzzle.

If you can image, this may be similar to your constant efforts to reinvent yourself in your line of business. Recursive marketing means you are trying new and innovative ideas to sustain your competitive advantage. Some paths are productive, some maybe mediocre or perhaps you are just matching the competition. So, you back up and try something different until you hit upon a solution that makes your competitor’s jaw drop to the floor.

I purchased one of the very first ever FAX computer adapter cards back in the late 80′s early 90′s, plugged my phone line in and started pumping out resumes from my Desktop for a very competitive position. Few geeks in my industry had these cards at the time let alone to think about applying for a job via this means. My phone started ringing off the hook. The resume was on top of the pile on the recruiters desk because it was first in the door, way in advance of other applicants. I had a competitive advantage and was early to the job. This was before job boards and email resume blasters. I was always trying something new and creative and I still do today.

In the real estate business or any business for that matter, your credentials are extremely important not only as customer facing information, but as well as to employers or vendors. Job boards are passe today and not very effective (my personal opinion), so now you have the specialty boards trying to charge monthly or annual fee. And as you all very well know the next thing is online social linking and networking. But, I just never get the very warm fuzzy feeling with respect to concrete results. So I keep looking for ways to reinvent myself in front of my customers.

In this age of Web 2.0 barely anyone understands what this is all about. How do I use it? How do I install it? What is it going to do for me? How does it compare or save me money against what I am shelling out already for search engine ranking? The terminology is just over powering (Semantic Web, Linked Data, RDF) and the list goes on.

Another, yet little known idea is emerging called “Expert Finding”. Now by gosh I want to be in on this because that is what I am selling. I continually hone my skills. And if you are going to fall into recursive marketing, you have to be open and forward thinking even if the techie stuff hurts your brain.

So, I am going to suggest to your forward thinking part of your brain a way to kill 2 birds with 1 stone. Both of these suggestions are complimentary and are not mutually exclusive.

1.Embark upon making your web site a Web 2.0 site with structured data.
2.Create a structured resume, a Semantic Web Friendly CV.

I will show you how I did it and how it looks. Remember, the whole purpose here it to have the Expert Finders find you first, directly and not have to look through a pile of clutter.

Go to my commercial real estate web site at http://www.propertyclubpro.com and click on the link called “John Wubbel Consultancy”. I have only 2 files on the web server called “Friend Of A Friend” or foaf.rdf and resume.rdf.

As the “John Wubbel Consultancy” page loads you will notice I am leveraging an Internet resource or tool from Zitgist. This is their Semantic Web browser or dataviewer. These RDF files are normally only used for machine to machine processing and semantic search engines. However, this wonderful resource from Zitgist gives a faceted view of foaf.rdf with a Google Map mashup, my photo and initial details. Consequentially, you gleen some knowledge about me. The Zitgist browser figured out “Linked Data”, (there is one of those strange terms), a link to my CV. View the bottom of the page and click on the resume hyperlink and the Zitgist browser analyses resume.rdf and allows you to learn more about me. Perhaps you can see how the web is the open database.

Now I am not so concerned that the format is not as pretty as my Word Processed PDF hard copy because I know that the knowledge bound in my resume as structured data will be specifically searchable on the Semantic Web by the Expert Finders.

My web site is now on the way to becoming a Web 2.0 site that is a landmark in the free space of the Internet. A Semantic Web Friendly CV is my newest recursive marketing move, an innovation to gain the edge on the competition, a respectable and subtle way for a customer to find the right person for the job and an opportunity to work on and close a sale.

John Wubbel
jwubbel@propertyclubpro.com