Marcin Maleszka

Integration computing and collective intelligence

Complete definition and description of integration function and postulates.Proposition of Aug function, which determines new knowledge gained in integration.Consistent description of multi-level integration and necessary postulates.Example of multi-level integration for two specific cases in tree integration.Multiple examples of integration function and criteria for trees. Techniques for processing knowledge in collectives are more and more needed because of rapidly increasing number of autonomous sources of knowledge in the world. Collective intelligence, among others, deals with creating the knowledge of a collective which is consistent and complete. This means that it should contain all elements not belonging to the knowledge of collective members, but can be inferred on the basis of knowledge of them. For this process the methodologies for knowledge integration seem to be very useful. In this paper the authors present a framework for integrating knowledge of a collective which shows that knowledge of a collective should not be a normal sum of knowledge of its members. The model for knowledge integration using complex hierarchical structures has been also presented and analyzed.

A METHOD FOR COMPLEX HIERARCHICAL DATA INTEGRATION

The process of integrating data in a hierarchical structure is usually based on algorithms designed for one specific task; for example, for eXtendable Markup Language (XML) schema integration. In this article, a more general approach for integration of data in a hierarchical structure is proposed using a generalized tree structure called a complex tree. For this aim we define a set of criteria for integration. These criteria are completeness, precision, and optimality. A simple algorithm based on these criteria is presented and the advantages of the approach are discussed.

Building Educational and Marketing Models of Diffusion in Knowledge and Opinion Transmission

Group communication and diffusion of information and opinion are important but unresearched aspect of collective intelligence. In this paper a number of hypotheses are proposed in discussed. Each hypothesis proven would be a considerable step towards creating a complete and coherent model of group communication, that could be used both in computer and human sciences. This paper also discusses some methodology that may be used by researchers to determine the hypotheses.

Path-Oriented integration method for complex trees

The hierarchical data format becomes increasingly common. One of the problems arising with it is efficient tree integration, i.e. integration of XML databases. One possible approach is path-based integration, used among others in applications using XQuery and XPath database queries. This article offers a generalization for integration tasks based on this approach, as well as a short analysis of the result in terms of other existing integration criteria. The paper presents estimations for such criteria as Completeness, Minimality and Precision.

A model for complex tree integration tasks

The common approach to integrating XML documents is based on existing formal structures, not originally designed to integration tasks. In this paper we propose a Complex Tree model designed from the beginning to integration tasks, capable of representing most tree structures. The Complex Tree model is defined on both Schema and Instance level, to better work in practical situations. The integration task for Complex Trees is also defined on both levels. A set of explicitly stated criteria for integration is given, to better design future integration algorithms, in respect of the desired aim of integration process. Finally a simple integration algorithm is presented, based on selected criteria.

A heuristic method for collaborative recommendation using hierarchical user profiles

Document recommendation in information retrieval is a well known problem. Recommending a profile in order to personalize document search is a less common approach. In this paper a specific solution to profile recommendation is proposed, by use of knowledge integration methods. A hierarchical user profile is defined to represent the user. For each new user joining an information retrieval system, a prepared non-empty profile is assigned based on other similar users. To create such a profile, knowledge integration methods are used. A set of postulates are proposed to describe such representative profile. Criteria measures are used to determine if a solution to a specific algorithm satisfies these postulates. Three integration algorithms are proposed and evaluated, including a heuristic algorithm. In future research, these algorithms will be used in a practical system.

Agent Technology for Information Retrieval in Internet

Searching for useful information is nowadays difficult because of the information overload problem in a global network consisting of heterogeneous sources. Intelligent information agents, or multiagent systems consisting of those, are one of the solutions to this problem. This paper is a short case study of existing information retrieval systems based on agent approaches. User profiles and searching issues are two main issues described and a functional categorization of independent profiling systems or independent searching systems is proposed, as well as a short description of a hybrid profiling and searching system.

The automatic summarization of text documents in the Cognitive Integrated Management Information System

This paper presents issues related to a process of the automatic summarization of the text documents connected with economic knowledge performed by the cognitive agents in an integrated management information system. In contemporary companies, the unstructured knowledge is essential, mainly due to the possibility of obtaining better flexibility and competitiveness of the organization. Therefore more often the decision are taken in the enterprises on the basis of the summaries. The first part of the paper shortly presents the state-of-the-art in the considered field; next, the summarization process in the Cognitive Integrated Management Information System is characterized; the case study related with the summaries generating agent is presented in the last part of this paper.

Approximate algorithms for solving o 1 consensus problems using complex tree structure

The consensus finding problem is known in the literature as a solution to inconsistency problems. Such inconsistency may come from different opinions of problem participants or data uncertainty. Consensus methods are used to find elements that represent all others in the inconsistent dataset and are a good compromise of the differing opinions. The O1 solution to consensus problem is best defined as finding the element that has the smallest sum of distances to all other elements. It is solved for many simple structures, but not for the complex tree structure. In this paper we propose several algorithms to find O1 consensus for complex trees (extended labeled trees), including a greedy algorithm and several approximate algorithms. We evaluate their approximation levels in terms of the 1-optimality criterion.

Some properties of complex tree integration criteria

Hierarchical data integration becomes important with the abundance of XML based solution in today world. We have previously introduced a model of hierarchical data - the Complex Tree, a model for its integration and some criteria to guide the integration process. In this paper some of those criteria are further analyzed and their properties are described: 1) the completeness criterion based integration may be decomposed into subtasks with the same final result, 2) the minimality criterion based integration may also be decomposed, but the result, while correct, is not guaranteed to be the same, 3) the precision and relationship completeness criteria are in some cases mutually exclusive.