Contributions published at Dynamic and Distributed Information Systems (Abraham Bernstein)

A critical need for many organizations in the next century will be the ability to quickly develop innovative business processes to take advantage of rapidly changing technologies and markets. Current process design tools and methodologies, however, are very resource-intensive and provide little support for generating (as opposed to merely recording) new design alternatives. This paper describes the Process Recombinator, a novel tool for generating new business process ideas by recombining elements from a richly structured repository of knowledge about business processes. The key contribution of the work is the technical demonstration of how such a repository can be used to automatically generate a wide range of innovative process designs. We have also informally evaluated the Process Recombinator in several field studies, which are briefly described here as well.

This paper describes a novel theoretical and empirical approach to tasks such as business process redesign and knowledge management. The project involves collecting examples of how different organizations perform similar processes, and organizing these examples in an on-line ìprocess handbook"". The handbook is intended to help people: (1) redesign existing organizational processes, (2) invent new organizational processes (especially ones that take advantage of information technology), and (3) share ideas about organizational practices. A key element of the work is an approach to analyzing processes at various levels of abstraction, thus capturing both the details of specific processes as well as the ""deep structure"" of their similarities. This approach uses ideas from computer science about inheritance and from coordination theory about managing dependencies. A primary advantage of the approach is that it allows people to explicitly represent the similarities (and differences) among related processes and to easily find or generate sensible alternatives for how a given process could be performed. In addition to describing this new approach, the work reported here demonstrates the basic technical feasibility of these ideas and gives one example of their use in a field study.

This article investigates how to support process enactment in highly flexible organizations. First it develops the requirements for such a support system. Then it proposes a prototype implementation, which offers its users the equivalent of a CAD/CAM-like tool for designing and supporting business processes. The tool enables end-users to take flexible building blocks of a production process, reassemble them to fit the specific needs of a particular case and finally export its description to process support systems like workflow management systems.

This paper addresses the classification of linked entities. We introduce a relational vector-space (VS) model (in analogy to the VS model used in information retrieval) that abstracts the linked structure, representing entities by vectors of weights. Given labeled data as background knowledge/training data, classification procedures can be defined for this model, including a straightforward, “direct” model using weighted adjacency vectors. Using a large set of tasks from the domain of company affiliation identification, we demonstrate that such classification procedures can be effective. We then examine the method in more detail, showing that as expected the classification performance correlates with the relational autocorrelation of the data set. We then turn the tables and use the relational VS scores as a way to analyze/visualize the relational autocorrelation present in a complex linked structure. The main contribution of the paper is to introduce the relational VS model as a potentially useful addition to the toolkit for relational data mining. It could provide useful constructed features for domains with low to moderate relational autocorrelation; it may be effective by itself for domains with high levels of relational autocorrelation, and it provides a useful abstraction for analyzing the properties of linked data.

This paper addresses the classification of linked entities. We introduce a relational vector-space (VS) model (in analogy to the VS model used in information retrieval) that abstracts the linked structure, representing entities by vectors of weights. Given labeled data as background knowledge/training data, classification procedures can be defined for this model, including a straightforward, “direct” model using weighted adjacency vectors. Using a large set of tasks from the domain of company affiliation identification, we demonstrate that such classification procedures can be effective. We then examine the method in more detail, showing that as expected the classification performance correlates with the relational autocorrelation of the data set. We then turn the tables and use the relational VS scores as a way to analyze/visualize the relational autocorrelation present in a complex linked structure. The main contribution of the paper is to introduce the relational VS model as a potentially useful addition to the toolkit for relational data mining. It could provide useful constructed features for domains with low to moderate relational autocorrelation; it may be effective by itself for domains with high levels of relational autocorrelation, and it provides a useful abstraction for analyzing the properties of linked data.

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Thousands of business news stories (including press releases, earnings reports, general business news, etc.) are released each day. Recently, information technology advances have partially automated the processing of documents, reducing the amount of text that must be read. Current techniques (e.g., text classification and information extraction) for full-text analysis for the most part are limited to discovering information that can be found in single documents. Often, however, important information does not reside in a single document, but in the relationships between information distributed over multiple documents. This paper reports on an investigation into whether knowledge can be discovered automatically from relational data extracted from large corpora of business news stories. We use a combination of information extraction, network analysis, and statistical techniques. We show that relationally interlinked patterns distributed over multiple documents can indeed be extracted, and (specifically) that knowledge about companies’ interrelationships can be discovered. We evaluate the extracted relationships in several ways: we give a broad visualization of related companies, showing intuitive industry clusters; we use network analysis to ask who are the central players, and finally, we show that the extracted interrelationships can be used for important tasks, such as classifying companies by industry membership.

The ability to rapidly locate useful on-line services (e.g. software applications, software components), as opposed to simply useful documents, is becoming increasingly critical in many domains. Current service retrieval technology is, however, notoriously prone to low precision. This paper describes a novel service retrieval approached based on the sophisticated use of process ontologies. Our preliminary evaluations suggest that this approach offers qualitatively higher retrieval precision than existing (keyword and table-based) approaches without sacrificing recall and computational tractability/scalability.

The ability to rapidly locate useful on-line services (e.g. software applications, software components, process models, or service organizations), as opposed to simply useful documents, is becoming increasingly critical in many domains. As the sheer number of such services increases it will become increasingly more important to provide tools that allow people (and software) to quickly find the services they need, while minimizing the burden for those who wish to list their services with these search engines. This can be viewed as a critical enabler of the ‘friction-free’ markets of the ‘new economy’. Current service retrieval technology is, however, seriously deficient in this regard. The information retrieval community has focused on the retrieval of documents, not services per se, and has as a result emphasized keyword-based approaches. Those approaches achieve fairly high recall but low precision. The software agents and distributed computing communities have developed simple ‘frame-based’ approaches for ‘matchmaking’ between tasks and on-line services increasing precision at the substantial cost of requiring all services to be modeled as frames and only supporting perfect matches. This paper proposes a novel, ontology-based approach that employs the characteristics of a process-taxonomy to increase recall without sacrificing precision and computational complexity of the service retrieval process.

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The ability to rapidly locate useful on-line services (e.g. software applications, software components, process models, or service organizations), as opposed to simply useful documents, is becoming increasingly critical in many domains. Current service retrieval technology is, however, notoriously prone to low precision. This paper describes a novel service retrieval approached based on the sophisticated use of process ontologies. Our preliminary evaluations suggest that this approach offers qualitatively higher retrieval precision than existing (keyword and table-based) approaches without sacrificing recall and computational tractability/scalability.

A data mining (DM) process involves multiple stages. A simple, but typical, process might include preprocessing data, applying a data-mining algorithm, and postprocessing the mining results. There are many possible choices for each stage, and only some combinations are valid. Because of the large space and non-trivial interactions, both novices and data-mining specialists need assistance in composing and selecting DM processes. We present the concept of Intelligent Discovery Assistants (IDAs), which provide users with (i) systematic enumerations of valid DM processes, in order that important, potentially fruitful options are not overlooked, and (ii) effective rankings of these valid processes by different criteria, to facilitate the choice of DM processes to execute. We use a prototype to show that an IDA can indeed provide useful enumerations and effective rankings. We dis-cuss how an IDA is an important tool for knowledge sharing among a team of data miners. Finally, we illustrate all the claims with a comprehensive demonstration using a more involved process and data from the 1998 KDDCUP competition.

Thousands of business news stories (including press releases, earnings reports, general business news, etc.) are released each day. Recently, information technology advances have partially automated the processing of documents, reducing the amount of text that must be read. Current techniques (e.g., text classification and information extraction) for full-text analysis for the most part are limited to discovering information that can be found in single documents. Often, however, important information does not reside in a single document, but in the relationships between information distributed over multiple documents. This paper reports on an investigation into whether knowledge can be discovered automatically from relational data extracted from large corpora of business news stories. We use a combination of information extraction, network analysis, and statistical techniques. We show that relationally interlinked patterns distributed over multiple documents can indeed be extracted, and (specifically) that knowledge about companies’ interrelationships can be discovered. We evaluate the extracted relationships in several ways: we give a broad visualization of related companies, showing intuitive industry clusters; we use network analysis to ask who are the central players, and finally, we show that the extracted interrelationships can be used for important tasks, such as classifying companies by industry membership.

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The ability to rapidly locate useful on-line services (e.g. software applications, software components, process models, or service organizations), as opposed to simply useful documents, is becoming increasingly critical in many domains. As the sheer number of such services increases it will become increasingly more important to provide tools that allow people (and software) to quickly find the services they need, while minimizing the burden for those who wish to list their services with these search engines. This can be viewed as a critical enabler of the ‘friction-free’ markets of the ‘new economy’. Current service retrieval technology is, however, seriously deficient in this regard. The information retrieval community has focused on the retrieval of documents, not services per se, and has as a result emphasized keyword-based approaches. Those approaches achieve fairly high recall but low precision. The software agents and distributed computing communities have developed simple ‘frame-based’ approaches for ‘matchmaking’ between tasks and on-line services increasing precision at the substantial cost of requiring all services to be modeled as frames and only supporting perfect matches. This paper proposes a novel, ontology-based approach that employs the characteristics of a process-taxonomy to increase recall without sacrificing precision and computational complexity of the service retrieval process.

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In the past, most collaboration support systems have focused on either automating fixed work processes or simply supporting communication in ad-hoc processes. This results in systems that are usually inflexible and difficult to change or that provide no specific support to help users decide what to do next. This paper describes a new kind of tool that bridges the gap between these two approaches by flexibly supporting processes at many points along the spectrum: from highly specified to highly unspecified. The development of this approach was strongly based on social science theory about collaborative work.

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