The National Technical University of Athens (NTUA) is the leading Technical University in Greece. The Computer Science Division of the Electrical and Computer Engineering Department covers several fields of practical, theoretical and technical computer science and is involved in several research projects supported by the EEC, the government and industrial companies. The Knowledge and Data Base Systems (KDBS) Laboratory was established in 1992 at the National Technical University of Athens. It is recognised internationally, evidenced by its participation as a central node in the Esprit Network of Excellence IDOMENEUS. The Information and Data on Open MEdia for NEtworks of USers, project aims to coordinate and improve European efforts in the development of next-generation information environments which will be capable of maintaining and communicating a largely extended class of information in an open set of media. The KDBS Laboratory employs one full-time research engineer and several graduate students. Its infrastructure includes a LAN with several DECstation 5000/200 and 5000/240 workstations, an HP Multimedia Workstation, several PCs and software for database and multimedia applications. The basic research interests of our Laboratory include: Spatial Database Systems, Multimedia Database Systems and Active Database Systems. Apart from the above database areas, interests of the KDBS Laboratory span several areas of Information Systems, such as Software Engineering Databases, Transactional Systems, Image Databases, Conceptual Modeling, Information System Development, Temporal Databases, Advanced Query Processing and Optimization Techniques. The group's efforts on Spatial Database Systems, include the study of new data structures, storage techniques, retrieval mechanisms and user interfaces for large geographic data bases. In particular, we look at specialized, spatial data structures (R-Trees and their variations) which allow for the direct access of the data based on their spatial properties, and not some sort of encoded representation of the objects' coordinates. We study implementation and optimization techniques of spatial data structures and develop models that make performance estimation. Finally, we are investigating techniques for the efficient representation of relationships and reasoning in space. The activities on Multimedia Database Systems, include the study of advanced data models, storage techniques, retrieval mechanisms and user interfaces for large multimedia data bases. The data models under study include the object-oriented model and the relational model with appropriate extensions to support multimedia data. We are also investigating content-based search techniques for image data bases. In a different direction, we are studying issues involved in the development of multimedia front-ends for conventional, relational data base systems. In the area of Active Database Systems, we are developing new mechanisms for implementing triggers in relational databases. Among the issues involved, we address the problem of efficiently finding qualifying rules against updates in large sets of triggers. This problem is especially critical in database system implementations of triggers, where large amounts of data may have to be searched in order to find out if a particular trigger may qualify to run or not. Continuing work that started at the Foundation for Research and Technology (FORTH), Institute of Computer Science, the group is investigating reuse-oriented approaches to information systems application development. The approaches are based on a repository that has been implemented at FORTH as a special purpose object store, with emphasis on multimodal and fast retrieval. Issues of relating and describing software artifacts (designs, code, etc.) are among the topics under investigation. A new important research direction of the group is on Data Warehouses, which are seen as collections of materialized views captured over a period of time from a heterogeneous distributed information system. Issues such as consistent updates, data warehouse evolution, view reconciliation and data quality are being investigated. Research in Image Databases deals with the retrieval by image content, that uses techniques from the area of Image Processing. We are currently at early stage in this direction, having collected many segmentation and edge detection algorithms, which will be used and evaluated in images of various contents. Our work on Advanced Query Processing and Optimization Techniques includes dynamic or parametric query optimization techniques. In most database systems, the values of many important runtime parameters of the system, the data, or the query are unknown at query optimization time. Dynamic, or parametric, query optimization attempts to identify several execution plans, each one of which is optimal for a subset of all possible values of the run time parameters. In the next sections we present in detail our research efforts on the three main research areas of the KDBS Laboratory: Spatial, Multimedia and Active Databases.

The subject of multi‐agent planning has been of continuing concern in Distributed Artificial Intelligence (DAI). In this paper, we suggest an approach to multi‐agent planning that contains heuristic elements. Our method makes use of subgoals, and derived sub‐plans, to construct a global plan. Agents solve their individual sub‐plans, which are then merged into a global plan. The suggested approach reduces overall planning time and derives a plan that approximates the optimal global plan that would have been derived by a central planner, given those original subgoals. We explore three different scenarios. The first involves a group of agents with a common goal. The second considers how agents can interleave planning and execution when planning towards a common, though dynamic, goal. The third examines the case where agents, each with their own goal, can plan together to reach a state in consensus for the group. Finally, we consider how these approaches can be adapted to handle rational, manipulative agents.

This paper adapts a procedure, canonical analysis of asymmetry, originally defined by Gower (1977) to the analysis of hierarchical properties in organizational structures. Its application is demonstrated using two available data sets, Freeman and Freeman�s (1980) data on computer communication and Krackhardt�s (1987) data on advice-giving and getting in an organization.

Multi-agent robotic systems are useful in many practical applications. For some tasks, such as holding a conference, {\it cooperation} among agents are necessary. For other tasks, such as cleaning a room, multiple agents can work in parallel for better performance. This paper provides help-based (HCP) and coordination-based (CCP) protocols for controlling agents to accomplish multi-agent tasks. The HCP utilizes efficient negotiation to coordinate agents into groups. The CCP improves overall performance by exchanging local knowledge among agents and making decisions in parallel. A reactive and modularized agent architecture was employed to implement the protocols. Since each protocol is embedded into the architecture, it is efficient and effective. In addition, the protocols are deadlock-free. The protocols were utilized to solve the Object-Sorting Task, which abstracts two characteristics of tasks: parallelism and cooperation. The experimental results showed that 1) both HCP and CCP are stable under different workload; 2) the protocols can effectively utilize the agent-power to achieve super-linear improvement; 3) The CCP is better than the HCP in both performance and speedup.

Intra-organizational network research had its first heyday during the empirical revolution in social sciences before World War II when it discovered the informal group within the formal organization. These studies comment on the classic sociological idea of bureaucracy being the optimal organization. Later relational interest within organizational studies gave way to comparative studies on the quantifiable formal features of organizations. There has been a resurgence in intra-organizational networks studies recently as the conviction grows that they are critical to organizational and individual performance. Along with methodological improvements, the theoretical emphasis has shifted from networks as a constraining force to a conceptualization that sees them as providing opportunities and finally, as social capital. Because of this shift it has become necessary not only to explain the differences between networks but also their outcomes, that is, their performance. It also implies that internal and external networks should no longer be treated separately. Research on differences between intra-organizational networks centers on the influence of the formal organization, organizational demography, technology and environment. Studies on outcomes deal with diffusion and adaptation of innovation; the utilization of human capital; recruitment, absenteeism and turnover; work stress and job satisfaction; equity; power; information efficiency; collective decision making; mobilization for and outcomes of conflicts; social control; profit and survival of firms and individual performance. Of all the difficulties that are associated with intra-organizational network research, problems of access to organizations and incomparability of research findings seem to be the most serious. Nevertheless, future research should concentrate on mechanisms that make networks productive, while taking into account the difficulties of measuring performance within organizations, such as the performance paradox and the halo-effect.

Amalthaea is an evolving, multi-agent ecosystem for personalized filtering, discovery, and monitoring of information sites. Amalthaea's primary application domain is the World Wide Web and its main purpose is to assist its users in finding interesting information. Two different categories of agents are introduced in the system: filtering agents that model and monitor the interests of the user and discovery agents that model the information sources.A market-like ecosystem where the agents evolve, compete, and collaborate is presented: agents that are useful to the user or other agents reproduce, while low-performing agents are destroyed. Results from various experiments with different system configurations and varying ratios of user interests versus agents in the system are presented. Finally issues like fine-tuning the initial parameters of the system and establishing and maintaining equilibria in the ecosystem are discussed.

This article discusses the relationship between the informal ties of the employees within an organization and their absentee rates.~To explain this relationship, the assumption is made that within a department, a more or less stable norm concerning illegal absenteeism exits, and that the strength of this norm is related to the tightness of the informal relationships between the employees: the more consensus on the absentee norm, irrespective of the degree of tolerance, the more cohesive this department. Furthermore, according to the fairness theory of Adams (1965), a relationship between the tightness of the informal relations, the norm concerning illegal absenteeism and the absentee rate is expected: the more cohesive, the higher the effect of the group norm on the short-term absentee rate. Network data of 62 employees of eight comparable teams within a housing corporation were collected by means of a questionnaire, and combined with data on the absentee rates of the employees. The data supported the hypotheses concerning the relationship between the group norm, the tightness of the informal relations and the short-term absentee rates of the employees within a department.

Three methodological issues are discussed that are important for the analysis of data on networks in organizations. The first is the two-level nature of the data: individuals are nested in organizations. This can be dealt with by using multilevel statistical methods. The second is the complicated nature of statistical methods for network analysis. The third issue is the potential of mathematical modeling for the study of network effects and network evolution in organizations. Two examples are given of mathematical models for gossip in organizations. The first example is a model for cross-sectional data, the second is a model for longitudinal data that reflect the joint development of network structure and individual behavior tendencies.

This paper proposes a multi-agent system that carries out cooperative work. To achieve this, we use Fuzzy Associative Memory Organizing Unit Systems (FAMOUS) and Conceptual Fuzzy Sets (CFS). By using these proposed methods, each agent robot can decide its own behaviour to suit the situation in its environment. We apply this system to an Intelligent Transportation System (ITS) and do simulations.

We propose a class of actor-oriented statistical models for closed social networks in general, and friendship networks in particular. The models are random utility models developed within a rational choice framework. Based on social psychological and sociological theories about friendship, mathematical functions capturing expected utility of individual actors with respect to friendship are constructed. Expected utility also contains a random (unexplained) component. We assume that, given their restrictions and contact opportunities, individuals evaluate their utility functions and behave such that they maximize the expected amount of utility. The behavior under consideration is the expression of like and dislike (choice of friends). Theoretical mechanisms that are modelled are, e.g., the principle of diminishing returns, the tendency towards reciprocated choices, and the preference for friendship relations with similar others. Constraints imposed on individuals are, e.g., the structure of the existing network, and the distribution of personal characteristics over the respondents. The models are illustrated by means of a data-set collected among university freshmen at 7 points in time during 1994 and 1995.

In this paper we analyze the evolution of solidarity relations between dissimilar actors by means of a cellular automaton framework. We assume that actors face two types of decisions in the course of an iterated game. First, actors� solidarity decisions constitute mutual support relations between neighbors. Second, by migrating in a two dimensional world, actors select between potential solidarity partners. Moreover, actors are dissimilar with respect to their neediness class, i.e., their need for help. Hegselmann (1996) demonstrated by computer simulation that under these assumptions the behavior of (boundedly) rational egoists may lead to the emergence of a solidarity network that is characterized by class segregation. In the present paper, we explore whether the macro phenomenon of segregation depends on the micro assumption of rationality. We replace Hegselmann�s rational egoist by an adaptive egoist, who takes solidarity and migration decisions on basis of the �law of effect�. A stochastic learning model (e.g., Flache and Macy, 1996) is used to simulate adaptive decision making. Our model of learning behavior, we show, entails the emergence of class segregated solidarity networks. At the same time, however, the evolving networks are considerably more fragile and less extended than those arising amongst rational egoists. While critics of the rational choice approach often argue that rational egoist models tend to underestimate the level of social solidarity, we showed that in this particular analysis relaxing the assumption of rationality may entail the prediction of less rather than more solidarity.

Knowledge discovery in databases is a rapidly growing field, whose development is driven by strong research interests as well as urgent practical, social, and economical needs. While the last few years knowledge discovery tools have been used mainly in research environments, sophisticated software products are now rapidly emerging. In this paper, we provide an overview of common knowledge discovery tasks and approaches to solve these tasks. We propose a feature classification scheme that can be used to study knowledge and data mining software. This scheme is based on the software's general characteristics, database connectivity, and data mining characteristics. We then apply our feature classification scheme to investigate 43 software products, which are either research prototypes or commercially available. Finally, we specify features that we consider important for knowledge discovery software to possess in order to accommodate its users effectively, as well as issues that are either not addressed or insufficiently solved yet.

This article presents the implementation of a maintenance application for cellular switching system using the multi-agent paradigm. The main philosophy behind the design of the multi-agent system is based on the TMN framework, where each agent can mapped with one or several TMN functional blocks.

Attribute-oriented generalization summarizes the information in a relational database by repeatedly replacing specific attribute values with more general concepts according to user-defined concept hierarchies. We introduce domain generalization graphs for controlling the generalization of a set of attributes and show how they are constructed. We then present serial and parallel versions of the Multi-Attribute Generalization algorithm for traversing the generalization state space described by joining the domain generalization graphs for multiple attributes. Based upon a generate-and-test approach, the algorithm generates all possible summaries consistent with the domain generalization graphs. Our experimental results show that significant speedups are possible by partitioning path combinations from the DGGs across multiple processors. We also rank the interestingness of the resulting summaries using measures based upon variance and relative entropy. Our experimental results also show that these measures provide an effective basis for analyzing summary data generated from relational databases. Variance appears more useful because it tends to rank the less complex summaries (i.e., those with few attributes and/or tuples) as more interesting.

This paper is a review of the book Discovering Data Mining: From Concept to Implementation -- Peter Cabena, Pablo Hadjinian, Rolf Stadler, Jaap Verhees, and Alessandro Zanasi (New Jersey: Prentice Hall, 195 pp., 1998).

In this paper we present an extension of logic programming (LP) that is suitable not only for the �rational� component of a single agent but also for the �reactive� component and that can encompass multi‐agent systems. We modify an earlier abductive proof procedure and embed it within an agent cycle. The proof procedure incorporates abduction, definitions and integrity constraints within a dynamic environment, where changes can be observed as inputs. The definitions allow rational planning behaviour and the integrity constraints allow reactive, condition‐action type behaviour. The agent cycle provides a resource‐bounded mechanism that allows the agent�s thinking to be interrupted for the agent to record and assimilate observations as input and execute actions as output, before resuming further thinking. We argue that these extensions of LP, accommodating multi‐theories embedded in a shared environment, provide the necessary multi‐agent functionality. We argue also that our work extends Shoham�s Agent0 and the BDI architecture.

The aim of the method presented in this paper is to support the acquisition of new knowledge and to enhance the interactions between knowledge workers. The approach chosen is to facilitate the sharing of those retrieval terms, which members of a community of practice have used to retrieve valuable information. The nature of information-seeking behaviour in on-line information sources is discussed and then the theory of formal concept analysis is introduced. It is subsequently shown how this theory can be applied to analyse the relations between documents and the retrieval terms that people use to access these documents. The result is a concept lattice that contains information on a community's information-seeking behaviour. The concept lattice uncovers relational and contextual information. Retrieval phrases are put into relational context depending on how they are associated by the documents that are of interest to a community of users. The contention made here is that such 'usage-based' structures will provide natural and intuitive access to information sources for communities of users. It is shown how this approach can be used to facilitate the sharing of the retrieval vocabulary to support the acquisition of new knowledge and to enhance the interactions within a community of practice.

Few empirical analyses have been done in the organizational researches of integrated healthcare networks (IHNs) or integrated healthcare delivery systems. Using a contingency derived context-process-performance model, this study attempts to explore the relationships among an IHN's strategic direction, structural design, and performance. A cross-sectional analysis of 100 IHNs suggests that certain contextual factors such as market competition and network age and tax status have statistically significant effects on the implementation of an IHN's service differentiation strategy, which addresses coordination and control in the market. An IHN's service differentiation strategy is positively related to its integrated structural design, which is characterized as integration of administration, patient care, and information system across different settings. However, no evidence supports that the development of integrated structural design may benefit an IHN's performance in terms of clinical efficiency and financial viability.

Today's typical Knowledge Management systems are not much different from document management systems. In both cases, the retrieval process involves entering a set of keywords and then browsing through a list of documents related to those keywords found by the systems. If Knowledge Management is to live up to its promises, a new generation of Knowledge Management-enabled applications has to be developed. The information has to be presented beyond just a list of documents. Applying data mining techniques to these systems is one of the few promising avenues that may yield a new set of applications. This paper describes our on-going research effort to extract and mine information from one of the largest private Knowledge Management systems in the world.

This paper presents a general C++ platform for the implementation of a trade network game (TNG) that combines evolutionary game play with preferential partner selection. In the TNG, successive generations of resource constrained traders choose and refuse trade partners on the basis of continually updated expected payoffs, engage in risky trades modelled as two-person games, and evolve their trade strategies over time. The modular design of the TNG platform facilitates experimentation with alternative specifications for market structure, trade partner matching, trading, expectation formation, and trade strategy evolution. The TNG platform can be used to study the evolutionary implications of these specifications at three different levels: individual trader attributes, trade network formation, and social welfare.

We consider a group of several non‐Bayesian agents that can fully coordinate their activities and share their past experience in order to obtain a joint goal in face of uncertainty. The reward obtained by each agent is a function of the environment state but not of the action taken by other agents in the group. The environment state (controlled by Nature) may change arbitrarily, and the reward function is initially unknown. Two basic feedback structures are considered. In one of them � the perfect monitoring case � the agents are able to observe the previous environment state as part of their feedback, while in the other � the imperfect monitoring case � all that is available to the agents are the rewards obtained. Both of these settings refer to partially observable processes, where the current environment state is unknown. Our study refers to the competitive ratio criterion. It is shown that, for the imperfect monitoring case, there exists an efficient stochastic policy that ensures that the competitive ratio is obtained for all agents at almost all stages with an arbitrarily high probability, where efficiency is measured in terms of rate of convergence. It is also shown that if the agents are restricted only to deterministic policies then such a policy does not exist, even in the perfect monitoring case.

With the growth in the size of datasets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, a greater recognition of the value of temporal and spatial data has been evident and the first papers looking at the confluence of these two areas are starting to emerge. This short paper provides a few comments on this research and provides a bibliography of relevant research papers investigating temporal, spatial and spatio-temporal data mining.

For many purposes, the Web page is too small a unit of interaction and analysis. Web sites are structured multimedia documents consisting of many pages, and users often are interested in obtaining and evaluating entire collections of topically related sites. Once such a collection is obtained, users face the challenge of exploring, comprehending and organizing the items. We report four innovations that address these user needs: (1) we replaced the Web page with the Web site as the basic unit of interaction and analysis;(2) we defined a new informationstructure, the clan graph, that groups together sets of related sites; (3) we augment the representation of a site with a site profile, information about site structure and content that helps inform user evaluation of a site; and (4) we invented a new graph visualization, the auditorium visualization, that reveals important structural and content properties of sites within a clan graph. Detailed analysis and user studies document the utility of this approach. The clan graph construction algorithm tends to filter out irrelevant sites and discover additional relevant items. The auditorium visualization, augmented with drill-down capabilities to explore site profile data, helps users to find high-quality sites as well as sites that serve a particular function.

Traditional organizational theory advocates increased differentiation and horizontal integration for organizations in unstable environments or with uncertain technologies. This paper seeks to develop a better understanding of the relationship of group structure and the level of interdependency between individuals on group performance under various task complexities. Complexity theory in general, and NK models in particular, are introduced as theoretical frameworks that offer an explanation for group performance. Simulation models are developed, based on the communication network research of Bavelas (1948) and Leavitt (1952), to explore the effects of decentralization and interdependence. The simulation model developed here shows general consistency with previous human subject experiments. However, contrary to predictions, not all decentralized group structures perform well when undertaking complex task assignments. Structures that are highly connected (actors communicating with all others) perform much worse than those with a lower level of connection. Further experiments varying both the number of actors and the degree of interdependence between them find evidence of the �edge of chaos.� This research advances our understanding of organizations beyond earlier models by suggesting that there is an optimal range of interconnectedness between actors or tasks that explains the variation in performance. An intriguing result is that this optimal level of interdependence is fairly low, regardless of the size of the group.

With over 800 million pages covering most areas of human endeavor, the World-wide Web is a fertile ground for data mining research to make a difference to the effectiveness of information search. Today, Web surfers access the Web through two dominant interfaces: clicking on hyperlinks and searching via keyword queries. This process is often tentative and unsatisfactory. Better support is needed for expressing one's information need and dealing with a search result in more structured ways than available now. Data mining and machine learning have significant roles to play towards this end.In this paper we will survey recent advances in learning and mining problems related to hypertext in general and the Web in particular. We will review the continuum of supervised to semi-supervised to unsupervised learning problems, highlight the specific challenges which distinguish data mining in the hypertext domain from data mining in the context of data warehouses, and summarize the key areas of recent and ongoing research.

The theory, principles and practice of multi-agent systems is typically characterised as a computational and engineering discipline, since it is through the medium of computational systems that artificial agent systems are most commonly expressed. However, most definitions of agency draw directly on non-computational disciplines for inspiration. During the 1999 UK workshop on multi-agent systems, UKMAS'99, we invited four speakers to address the conceptualisation of multi-agent systems from their perspective as non-computer scientists. This paper presents their arguments and summarises some of the key points of discussion during the panel.

Data mining has become an important technique which has tremendous potential in many commercial and industrial applications. Attribute-oriented induction is a powerful mining technique and has been successfully implemented in the data mining system DBMiner (Han et al. Proc. 1996 Int'l Conf. on Data Mining and Knowledge Discovery (KDD'96), Portland, Oregon, 1996). However, its induction capability is limited by the unconditional concept generalization. In this paper, we extend the concept generalization to rule-based concept hierarchy, which enhances greatly its induction power. When previously proposed induction algorithm is applied to the more general rule-based case, a problem of induction anomaly occurs which impacts its efficiency. We have developed an efficient algorithm to facilitate induction on the rule-based case which can avoid the anomaly. Performance studies have shown that the algorithm is superior than a previously proposed algorithm based on backtracking.

Many researchers have investigated the social and cognitive processes underlying organizational behavior, with particular interest in understanding the interaction between the social and cognitive dimensions. Because of the widespread use of graphs as models of social and cognitive structures, these studies frequently encounter the problem of analyzing collections of graphical structures. Such analyses have used a variety of approaches to address specific aspects of such structures. However, no single unified approach has emerged that supports the several different types of analyses required. The purpose of this paper is to define such an approach, based on a mathematical model for capturing the relationships among multiple graphs, and to demonstrate its application to the investigation of social and cognitive structures in organizations.

The paper presents a set of requirements for a data mining system for mining remotely sensed satellite data based on a number of taxonomies that characterize mining of such data. The first of these taxonomies is based on knowledge of the mining objectives and mining algorithms. The second is based on various relationships that are found in data, including those between different types of data, different spatial locations of the data and different times of data capture. The paper then describes the ADaM data mining system, which was developed to address these requirements. The paper describes several data mining techniques that have been applied to remotely sensed data. The first type is target independent mining, which mines data for transients and trends, with mined results representing a highly concentrated form of the original data. The second type is the mining of vectors (representing multi-spectral or fused data) for association rules representing relationships between the various types of data represented by the elements of the vector. The third type mines data for association rules that characterize the texture of the data.

Often scientists need to locate appropriate software for their problems and then select from among many alternatives. We have previously proposed an approach for dealing with this task by processing performance data of the targeted software. This approach has been tested using a customized implementation referred to as PYTHIA. This experience made us realize the complexity of the algorithmic discovery of knowledge from performance data and of the management of these data together with the discovered knowledge. To address this issue, we created PYTHIA-II�a modular framework and system which combines a general knowledge discovery in databases (KDD) methodology and recommender system technologies to provide advice about scientific software/hardware artifacts. The functionality and effectiveness of the system is demonstrated for two existing performance studies using sets of software for solving partial differential equations. From the end-user perspective, PYTHIA-II allows users to specify the problem to be solved and their computational objectives. In turn, PYTHIA-II (i) selects the software available for the user's problem (ii) suggests parameter values, and (iii) assesses the recommendation provided. PYTHIA-II provides all the necessary facilities to set up database schemas for testing suites and associated performance data in order to test sets of software. Moreover, it allows easy interfacing of alternative data mining and recommendation facilities. PYTHIA-II is an open-ended system implemented on public domain software and has been used for performance evaluation in several different problem domains.

Current research on distributed knowledge processes suggests a critical conflict between knowledge processes in groups and the technologies built to support them. The conflict centers on observations that authentic and efficient knowledge creation and sharing is deeply embedded in an interpersonal face to face context, but that technologies to support distributed knowledge processes rely on the assumption that knowledge can be made mobile outside these specific contexts. This conflict is of growing national importance as work patterns change from same site to separate site collaboration, and millions of government and industrial dollars are invested in establishing academic-industry alliances and building infrastructures to support distributed collaboration and knowledge. ?In this paper we describe our multi-method approach for studying the tension between embedded and mobile knowledge in a project funded by the National Science Foundation�s program on Knowledge and Distributed Intelligence. This project examines knowledge processes and technology in distributed, multidisciplinary scientific teams in the National Computational Science Alliance (Alliance), a prototypical next generation enterprise. First we review evidence for the tension between embedded and mobile knowledge in several research literatures. Then we present our three-factor conceptualization that considers how the interrelationships among characteristics of the knowledge shared, group context, and communications technology contribute to the tension between embedded and mobile knowledge. Based on this conceptualization we suggest that this dichotomy does not fully explain distributed multidisciplinary knowledge processes. Therefore we propose some alternate models of how knowledge is shared. We briefly introduce the setting in which we are studying distributed knowledge processes and finally, we describe the data collection methods and the current status of the project.

This paper raises the question, �What is an effective representation framework for organizational process design?� By combining our knowledge of existing process models with data from a field study, the paper develops criteria for an effective process representation. Using these criteria and the case study, the paper integrates the process redesign and information system literatures to develop a representation framework that captures a process' social context. The paper argues that this social context framework, which represents people's motivations, social relationships, and social constraints, gives redesigners a richer sense of the process and allows process redesigners to simultaneously change social and logistic systems. The paper demonstrates the framework and some of its benefits and limitations.

This paper surveys applications of data mining techniques to large text collections, and illustrates how those techniques can be used to support the management of science and technology research. Specific issues that arise repeatedly in the conduct of research management are described, and a textual data mining architecture that extends a classic paradigm for knowledge discovery in databases is introduced. That architecture integrates information retrieval from text collections, information extraction to obtain data from individual texts, data warehousing for the extracted data, data mining to discover useful patterns in the data, and visualization of the resulting patterns. At the core of this architecture is a broad view of data mining�the process of discovering patterns in large collections of data�and that step is described in some detail. The final section of the paper illustrates how these ideas can be applied in practice, drawing upon examples from the recently completed first phase of the textual data mining program at the Office of Naval Research. The paper concludes by identifying some research directions that offer significant potential for improving the utility of textual data mining for research management applications.

In this paper, we extend the scope of mining association rules from traditional single-dimensional intratransaction associations, to multidimensional intertransaction associations. Intratransaction associations are the associations among items with the same transaction, where the notion of the transaction could be the items bought by the same customer, the events happened on the same day, and so on. However, an intertransaction association describes the association relationships among different transactions, such as �if(company) A's stock goes up on day 1, B's stock will go down on day 2, but go up on day 4.� In this case, whether we treat company or day as the unit of transaction, the associated items belong to different transactions. Moreover, such an intertransaction association can be extended to associate multiple contextual properties in the same rule, so that multidimensional intertransaction associations can be defined and discovered. A two-dimensional intertransaction association rule example is �After McDonald and Burger King open branches, KFC will open a branch two months later and one mile away,� which involves two dimensions: time and space. Mining intertransaction associations poses more challenges on efficient processing than mining intratransaction associations. Interestingly, intratransaction association can be treated as a special case of intertransaction association from both a conceptual and algorithmic point of view. In this study, we introduce the notion of multidimensional intertransaction association rules, study their measurements�support and confidence�and develop algorithms for mining intertransaction associations by extension of Apriori. We overview our experience using the algorithms on both real-life and synthetic data sets. Further extensions of multidimensional intertransaction association rules and potential applications are also discussed.

As the size of data warehouses increase to several hundreds of gigabytes or terabytes, the need for methods and tools that will automate the process of knowledge extraction, or guide the user to subsets of the dataset that are of particular interest, is becoming prominent. In this survey paper we explore the problem of identifying and extracting interesting knowledge from large collections of data residing in data warehouses, by using data mining techniques. Such techniques have the ability to identify patterns and build succinct models to describe the data. These models can also be used to achieve summarization and approximation. We review the associated work in the OLAP, data mining, and approximate query answering literature. We discuss the need for the traditional data mining techniques to adapt, and accommodate the specific characteristics of OLAP systems. We also examine the notion of interestingness of data, as a tool to guide the analysis process. We describe methods that have been proposed in the literature for determining what is interesting to the user and what is not, and how these approaches can be incorporated in the data mining algorithms.

Intelligent Networks (IN) are used in telecommunication networks to provide services that require a decision-making network element. The Service Control Point (SCP) can be overloaded when the number of service requests exceeds the SCPs designed capacity. Traditional IN load control algorithms assume a single service network model or use a centralized controller to find a solution. In this paper we propose and investigate a market-based model, in the form of a computational economy, for solving the distributed IN load control problem for a multi-service network. We investigate two algorithms, one price-oriented and the other resource-oriented, for finding the competitive equilibrium for this economy. We conclude that the price-oriented approach generally performs better and allows a greater level of distributed-decision making but suffers from an infeasible solution in real-time systems. Furthermore, we study a realization of this model as a multi-agent system (MAS) and investigate the communication overhead associated with running auctions for services.

The main architectures, learning abilities and applications of radial basis function (RBF) neural networks are well documented. However, to the best of our knowledge, no in-depth analyses have been carried out into the influence on the behaviour of the neural network arising from the use of different alternatives for the design of an RBF (different non-linear functions, distances, number of neurons, structures, etc.). Thus, as a complement to the existing intuitive knowledge, it is necessary to have a more precise understanding of the significance of the different alternatives. In the present contribution, the relevance and relative importance of the parameters involved in such a design are investigated by using a statistical tool, the ANalysis Of the VAriance (ANOVA). In order to obtain results that are widely applicable, various problems of classification, functional approximation and time series estimation are analyzed. Conclusions are drawn regarding the whole set.

Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Web usage mining consists of three phases, namely preprocessing, pattern discovery, and pattern analysis. This paper describes each of these phases in detail. Given its application potential, Web usage mining has seen a rapid increase in interest, from both the research and practice communities. This paper provides a detailed taxonomy of the work in this area, including research efforts as well as commercial offerings. An up-to-date survey of the existing work is also provided. Finally, a brief overview of the WebSIFT system as an example of a prototypical Web usage mining system is given.

Knowledge and knowledge management are receiving tremendous interest from both practitioners and academics. Although knowledge management is often accepted as a very useful organizational activity, a number of the assumptions underlying knowledge management have not been investigated. This paper examines four knowledge management assumptions: knowledge is worth managing, organizations benefit from managing knowledge, knowledge can be managed, and little risk is associated with managing knowledge. The assumptions are analyzed at strategic and operational levels, and both negating and supporting evidence is presented. Based on this analysis, a framework for research in knowledge management is proposed. The framework is used to generate a number of key questions that should be addressed in knowledge management research. Particular attention is given to goals and rewards as well as to the role of information technology in knowledge management.

Knowledge discovery from data sets can be extensively automated by using data mining software tools. Techniques for mining series of interval events, however, have not been considered. Such time series are common in many applications. In this paper, we propose mining techniques to discover temporal containment relationships in such series. Specifically, an item A is said to contain an item B if an event of type B occurs during the time span of an event of type A, and this is a frequent relationship in the data set. Mining such relationships provides insight about temporal relationships among various items. We implement the technique and analyze trace data collected from a real database application. Experimental results indicate that the proposed mining technique can discover interesting results. We also introduce a quantization technique as a preprocessing step to generalize the method to all time series.

This paper presents the PlanMine sequence mining algorithm to extract patterns of events that predict failures in databases of plan executions. New techniques were needed because previous data mining algorithms were overwhelmed by the staggering number of very frequent, but entirely unpredictive patterns that exist in the plan database. This paper combines several techniques for pruning out unpredictive and redundant patterns which reduce the size of the returned rule set by more than three orders of magnitude. PlanMine has also been fully integrated into two real-world planning systems. We experimentally evaluate the rules discovered by PlanMine, and show that they are extremely useful for understanding and improving plans, as well as for building monitors that raise alarms before failures happen.

In recent years, multi-agent systems (MASs) have received increasing attention in the artificial intelligence community. Research in multi-agent systems involves the investigation of autonomous, rational and flexible behaviour of entities such as software programs or robots, and their interaction and coordination in such diverse areas as robotics (Kitano et al., 1997), information retrieval and management (Klusch, 1999), and simulation (Gilbert & Conte, 1995). When designing agent systems, it is impossible to foresee all the potential situations an agent may encounter and specify an agent behaviour optimally in advance. Agents therefore have to learn from, and adapt to, their environment, especially in a multi-agent setting.

The area of software agents has experienced an exponential growth during the past decade, and is now being given a further boost by the introduction of global distributed computing services, such as Globus and Legion. The focus of the research has gradually shifted from single agent architectures to multi-agent systems and agent societies. In a Grid computing environment, agents should be able to discover efficiently other agents based on the computational services they offer or their characteristics (agent discovery). Existing systems either ignore this issue or use simplistic organisation models, which act as passive �yellow pages� thus keeping the discovery process separate from the computation. In this paper it is argued that the agent discovery can be coupled with several aspects of the computation such as access control and customisation resulting in a better sharing, use and management of the information held by the agent discovery system. A novel architecture is presented in which discovery messages and discovery paths are mutable, active entities, which interact with each other as peers making the organisation system dynamic in nature. Queries traversing the system can be reformulated while at the same time the system itself can change depending on the nature and volume of the query traffic. Furthermore it is shown that the organisation nodes of the proposed architecture can serve as re-usable components for building more complex, composite nodes from existing ones.

A fundamental task in data analysis is understanding the differences between several contrasting groups. These groups can represent different classes of objects, such as male or female students, or the same group over time, e.g. freshman students in 1993 through 1998. We present the problem of mining contrast sets: conjunctions of attributes and values that differ meaningfully in their distribution across groups. We provide a search algorithm for mining contrast sets with pruning rules that drastically reduce the computational complexity. Once the contrast sets are found, we post-process the results to present a subset that are surprising to the user given what we have already shown. We explicitly control the probability of Type I error (false positives) and guarantee a maximum error rate for the entire analysis by using Bonferroni corrections.

This short article summarizes the program of the Sixth Workshop on Research Issues in Data Mining and Knowledge Discovery Workshop (DMKD 2001).

The concept of holonic systems has its roots in the desire to understand the structure of natural systems (e.g. living organisms and social organisations) and in particular their ability to behave in a stable yet flexible manner in the face of change. It is not surprising that the lessons learned from these natural systems could help with the design and control of complex man-made systems. However, a key issue is, how can one translate holonic concepts to real industrial environments? For example, one of the key holonic concepts, the holon, can be described as a self-contained autonomous and cooperative entity; when deciding how to implement holons, software agents appear to be the logical choice. In this paper, we summarise the presentations and discussions from a workshop held at the recent International Conference on Autonomous Agents that focused on this issue and brought together researchers from both the holonic systems and the multi-agents systems communities.

Mining association rules from large databases is very costly. We propose to develop parallel algorithms for this task on shared-memory multiprocessor (SMP). All proposed parallel algorithms for other paradigms follow the conventional level-wise approach: they need as many iterations as the length of the maximum large itemset. To make matter worse, they impose a synchronization in every iteration which would cause serious I/O contention on shared-memory parallel system. An adaptive asynchronous parallel mining algorithm APM has been proposed for SMP. All processors generate candidates dynamically and count itemset supports independently without synchronization. Two optimization techniques have been proposed for the reduction of database scanning and the number of candidates. The algorithm APM has been implemented on a Sun Enterprise 4000 shared-memory multiprocessor with 12 nodes. The experiments show that the optimizations have very good effects and APM has a substantial lead in performance over other proposed level-wise algorithms.

There is little doubt that the strength and breadth of UK research into multi-agent systems continues to grow as we move into the new millennium. In the middle of an extremely cold December in 2000, the Third UK Workshop on Multi-Agent Systems (UKMAS 2001) was held at St Catherine's College, Oxford. This was the fifth such meeting in as many years, generously sponsored by EPSRC, FIPA (The Foundation for Intelligent Physical Agents) and Hewlett Packard.

A new graph theoretical algorithm to calculate Katz status scores reduces computational complexity from time i>O(i>n3) to i>O(i>n + i>m). Randomly-generated graphs as well as data from a large empiric study are used to test the performance of two commercial network analysis packages (GRADAP and UCINET V), compared to the performance achieved by the authors' algorithm, implemented in Visual Basic.

We discuss data mining based on association rules for two numeric attributes and one Boolean attribute. For example, in a database of bank customers, Age and Balance are two numeric attributes, and CardLoan is a Boolean attribute. Taking the pair (Age, Balance) as a point in two-dimensional space, we consider an association rule of the form Age,Balance ?P?CardLoan =Yes, which implies that bank customers whose ages and balances fall within a planar region P tend to take out credit card loans with a high probability.We consider two classes of regions, rectangles and admissible (i.e., connected and x-monotone) regions. For each class, we propose efficient algorithms for computing the regions that give optimal association rules for gain, support, and confidence, respectively. We have implemented the algorithms for admissible regions as well as several advanced functions based on them in our data mining system named SONAR (System for Optimized Numeric Association Rules), where the rules are visualized by using a graphic user interface to make it easy for users to gain an intuitive understanding of rules.

We address the issue of rational communicative behavior among autonomous self-interested agents that have to make decisions as to what to communicate, to whom, and how. Following decision theory, we postulate that a rational speaker should design a speech act so as to optimize the benefit it obtains as the result of the interaction. We quantify the gain in the quality of interaction in terms of the expected utility, and we present a framework that allows an agent to compute the expected utilities of various communicative actions. Our framework uses the Recursive Modeling Method as the specialized representation used for decision-making in a multi-agent environment. This representation includes information about the agent's state of knowledge, including the agent's preferences, abilities and beliefs about the world, as well as the beliefs the agent has about the other agents, the beliefs it has about the other agents' beliefs, and so on. Decision-theoretic pragmatics of a communicative act can be then defined as the transformation the act induces on the agent's state of knowledge about its decision-making situation. This transformation leads to a change in the quality of interaction, expressed in terms of the expected utilities of the agent's best actions before and after the communicative act. We analyze decision-theoretic pragmatics of a number of important kinds of communicative acts and investigate their expected utilities using examples. Finally, we report on the agreement between our method of message selection and messages that human subjects choose in various circumstances, and show an implementation and experimental validation of our framework in a simulated multi-agent environment.

In this paper a reusable multi-agent architecture for intelligent Websites is presented and illustrated for an electronic department store. The architecture has been designed and implemented using the compositional design method for multi-agent systems DESIRE. The agents within this architecture are based on a generic information broker agent model. It is shown how the architecture can be exploited to design an intelligent Website for insurance, developed in co-operation with the software company Ordina Utopics and an insurance company.

During the last two decades of the twentieth century we have seen various transformations in our society as a whole. In particular, information and communication technologies (ICTs) have played a critical role in this transformation process. Because of their pervasiveness and our intensive use of them, ICTs have changed our ways of living in virtually all realms of our social lives. ICT is of course not the sole factor of this transformation; various "old" technologies have also played a significant part. Modern transportation technologies, for example, have become dramatically sophisticated in terms of effectiveness and usefulness since the early twentieth century. The train and airline infrastructures are highly integrated with ICTs such as electronic reservation systems and traffic control systems. It is therefore important to recognize that the fundamental nature of technological revolution in the late twentieth century is the dynamic and complex interplay between old and new technologies and between the reconfiguration of the technological fabric and its domestication [6, 27, 32, 40].This paper concerns the concept of mobility, which manifests such a transformation of our social lives combining new and old technologies. It is now widely argued that our life styles have become increasingly mobile in the sense that the speed of transportation and hence geographical reach within a given time span is dramatically augmented by modern technological developments and sophistication such as train and airplane systems. However, in spite of the upsurge of concern with mobility in our social lives, current research perspectives define the notion of mobility quite narrowly, exclusively in terms of humans' independency from geographical constraints. For example, Makimoto and Manners [28] argue that within the next decade or so, a large part of the facilities and tools at home and in the office will be reduced enough in size to be carried, making people "geographically independent" (p. 2) and that people who use such mobile technologies, it is claimed, will be "free to live where they want and travel as much as they want" (p. 6). Their arguments for the significance of mobility, or nomadicity, are clearly confined to the corporeal characteristic of human movement freed from geographical constraints thanks to mobile computing technologies and services such as mobile phones and personal digital assistants (PDAs). Likewise, most of research on mobility in the Computer Supported Cooperative Work (CSCW) field has been showing the same tendency [e.g. 5, 11].Considering such a confined situation of the debates on mobility looking only at human geographical movement, we reconsider in this paper the notion of mobility and try to expand our perspective towards it. To do so, we argue that "being mobile" is not just a matter of people traveling but, far more importantly, related to the interaction they perform --- the way in which they interact with each other in their social lives. New configurations of social-technical relationships resulting from the diffusion of ICTs afford various dimensions of mobility to humans' interactivity with others in their social lives. We here suggest expanding the concept of mobility by looking at three distinct dimensions of human interaction; namely, spatial, temporal and contextual mobility. These three dimensions of human interaction have been dramatically mobilized by intensive use of ICTs, especially mobile technologies, in our social lives in general and work environments in particular. In the following, we will discuss each of these three dimensions in detail and implications for future debates on mobility will be drawn.