Search

The main contribution of this paper is an initial method for automatically exploiting ontology design patterns with the aim of further automating the creation of enterprise ontologies in small-scale application contexts. The focus is so far on developing a fully automated construction method, thereby somewhat reducing the requirements on ontology customization and level of detail. In this paper we present an approach how to use knowledge (patterns) from other areas, like data modeling, knowledge reuse, software analysis and software design, to create ontology patterns. These design patterns are then used within our method for automatically matching and pruning them, in accordance with information extracted from existing knowledge sources within the company in question. Though the method still needs some fine-tuning, it has already been used when creating an enterprise ontology for a supplier-company within the automotive industry. [ABSTRACT FROM AUTHOR]

It is well accepted that different types of distributed architectures require different levels of coupling. For example, in client-server and three-tier architectures the application components are generally tightly coupled between them and with the underlying communication middleware. Meanwhile, in off-line transaction processing, grid computing and mobile application architectures, the degree of coupling between application components and with the underlying middleware needs to be minimised along different dimensions. In the literature, terms such as synchronous, asynchronous, blocking, non-blocking, directed, and non-directed are generally used to refer to the degree of coupling required by a given architecture or provided by a given middleware. However, these terms are used with various connotations by different authors and middleware vendors. And while several informal definitions of these terms have been provided, there is a lack of an overarching framework with a formal grounding upon which software architects can rely to unambiguously communicate architectural requirements with respect to coupling. This paper addresses this gap by: (i) identifying and formally defining three dimensions of coupling; (ii) relating these dimensions to existing communication middleware; and (iii) proposing notational elements for representing coupling configurations. The identified dimensions provide the basis for a classification of middleware which can be used as a selection instrument. [ABSTRACT FROM AUTHOR]

Non-trivial search predicates beyond mere equality are at the current focus of P2P research. Structured queries, as an important type of non-trivial search, have been studied extensively mainly for unstructured P2P systems so far. As unstructured P2P systems do not use indexing, structured queries are very easy to implement since they can be treated equally to any other type of query. However, this comes at the expense of very high bandwidth consumption and limitations in terms of guarantees and expressiveness that can be provided. Structured P2P systems are an efficient alternative as they typically offer logarithmic search complexity in the number of peers. Though the use of a distributed index (typically a distributed hash table) makes the implementation of structured queries more efficient, it also introduces considerable complexity, and thus only a few approaches exist so far. In this paper we present a first solution for efficiently supporting structured queries, more specifically, XPath queries, in structured P2P systems. For the moment we focus on supporting queries with descendant axes ("//") and wildcards ("*") and do not address joins. The results presented in this paper provide foundational basic functionalities to be used by higher-level query engines for more efficient, complex query support. [ABSTRACT FROM AUTHOR]

This paper describes a Java-based communication middleware, called AspectJRMI, that applies aspect-oriented programming concepts to achieve the following requirements: (1) modular implementation of its features, including those with a crosscutting behavior; (2) high degree of configurability and adaptability; (3) performance similar to conventional object-oriented communication middleware systems, such as CORBA and Java RMI. In AspectJRMI, users may explicitly select the features provided by the middleware infrastructure, according to their needs. Most of these features have a crosscutting behavior, including interceptors, oneway calls, asynchronous calls, value-result parameter passing, and collocation optimizations. In this case, they are implemented as aspects. The design of AspectJRMI follows a set of principles, called horizontal decomposition, to achieve pluggability of aspects to the core middleware implementation. This paper presents the programming interface and the implementation of AspectJRMI. It also presents experimental results of its performance. [ABSTRACT FROM AUTHOR]

Despite the overwhelming popularity of web-based distri-buted systems, in certain areas the traditional desktop applications still seem to be a better choice. In this paper we examine the challenges involved in developing a secure and fault-tolerant data persistence layer for distributed desktop applications. We analyse the currently existing persistence frameworks and show why they do not meet the requirements set by certain distributed applications. The body of this paper concentrates on the Offline Business Objects framework which aims to fill this gap. The framework introduces the offline operation paradigm, whereby client applications operate without a permanent server connection and only periodically synchronise their data with the central database. We successfully deployed a distributed information system based on this paradigm and the Offline Business Objects for five major cities in Poland, which confirmed the practical value of our approach. [ABSTRACT FROM AUTHOR]

Although conventional Web search environments are very useful for well-structured, closed-ended questions, they become less efficient in dealing with ill-structured, open-ended questions. This paper presents design rationales and implementations of an alternative Web search environment called VisSearch. The VisSearch environment adopts visualization techniques to reduce various cognitive loads of iterative Web searches. It also employs a data mining algorithm to utilize Web search results of others. This paper describes in detail how these techniques can be used to facilitate efficient Web searches for ill-structured, open-ended questions, thereby fostering creation of a community of knowledge on the Web. [ABSTRACT FROM AUTHOR]

The paper investigates the logic of database transformations based on abstract metafinite state machines. We first introduce a structure model that separates a metafinite state into database and algorithmic spaces with bridge functions providing a connection between them. Then abstract metafinite state machines are developed on top of metafinite states equipped with two kinds of updates: exclusive updates and aggregate updates. In order to characterize both static and dynamic aspects of abstract metafinite state machines, we present a logic which supports reasoning about the side effects of exclusive and aggregate updates in update multisets occurring over database transformations. [ABSTRACT FROM AUTHOR]

The paper presents a sound and strongly complete axiomatization of reasoning about polynomial weight formulas. In addition, the PSPACE decision procedure for polynomial weight formulas developed by Fagin, Halpern and Megiddo works for our logic as well. The introduced formalism allows the expression of qualitative probability statements, conditional probability and Bayesian inference. [ABSTRACT FROM AUTHOR]

Visibly Pushdown Languages (VPLs), recognized by Visibly Pushdown Automata (VPAs), are a nicely behaved family of context-free languages. It has been shown that VPAs are equivalent to Extended Document Type Definitions (EDTDs), and thus, they provide means for elegantly solving various problems on XML. Especially, it has been shown that VPAs are the apt device for streaming XML. One of the important problems about XML that can be addressed using VPAs is the validation problem in which we need to decide whether an XML document conforms to the specification given by an EDTD. In this paper, we are interested in solving the approximate version of this problem, which is to decide whether an XML document can be modified by a tolerable number of edit operations to yield a valid one with respect to a given EDTD. For this, we define Visibly Pushdown Transducers (VPTs) that give us the framework for solving this problem under two different semantics for edit operations on XML. While the first semantics is a generalization of edit operations on strings, the second semantics is new and motivated by the special nature of XML documents. Usings VPTs, we give streaming algorithms that solve the problem under both the semantics. These algorithms use storage space that only depends on the size of the EDTD and the number of tolerable errors. Furthermore, they can check approximate validity of an incoming XML document in a single pass over the document, using auxilliary stack space that is proportional to the depth of the XML document. [ABSTRACT FROM AUTHOR]

The paper presents a new approach to database preferences queries, where preferences are represented in a possibilistic logic manner, using symbolic weights. The symbolic weights may be processed without assessing their precise value, which leaves the freedom for the user to not specify any priority among the preferences. The user may also enforce a (partial) ordering between them, if necessary. The approach can be related to the processing of fuzzy queries whose components are conditionally weighted in terms of importance. Here, importance levels are symbolically processed, and refinements of both Pareto ordering and minimum ordering are used. The representational power of the proposed setting is stressed, while the approach is compared with database Best operator-like methods and with the CP-net approach developed in artificial intelligence. The paper also provides a structured and rather broad overview of the different lines of research in the literature dealing with the handling of preferences in database queries. [ABSTRACT FROM AUTHOR]

In this paper we recast the formalism of argumentation formalism known as DeLP (Defeasible Logic Programming) in game-theoretic terms. By considering a game between a Proponent and an Opponent, in which they present arguments for and against each literal we obtain a bigger gamut of truth values for those literals and their negations as they are defended and attacked. An important role in the determination of warranted literals is assigned to a defeating relation among arguments. We consider first an unrestricted version in which these games may be infinite and then we analyze the underlying assumptions commonly used to make them finite. Under these restrictions the games are always determined -one of the players has a winning strategy. We show how varying the defeating relation may alter the set of truth values reachable under this formalism. We also show how alternative characterizations of the defeating relation may lead to different assignations of truth values to the literals in a DeLP program. [ABSTRACT FROM AUTHOR]

In this paper, we propose a probabilistic framework targeting three important issues in the computation of quality and trust in decentralized systems. Specifically, our approach addresses the multi-dimensionality of quality and trust, taking into account credibility of the collected data sources for more reliable estimates, while also enabling the personalization of the computation. We use graphical models to represent peers' qualitative behaviors and exploit appropriate probabilistic learning and inference algorithms to evaluate their quality and trustworthiness based on related reports. Our implementation of the framework introduces the most typical quality models, uses the Expectation-Maximization algorithm to learn their parameters, and applies the Junction Tree algorithm to inference on them for the estimation of quality and trust. The experimental results validate the advantages of our approach: first, using an appropriate personalized quality model, our computational framework can produce good estimates, even with a sparse and incomplete recommendation data set; second, the output of our solution has well-defined semantics and useful meanings for many purposes; third, the framework is scalable in terms of performance, computation, and communication cost. Furthermore, our solution can be shown as a generalization or serve as the theoretical basis of many existing trust computational approaches. [ABSTRACT FROM AUTHOR]

In this paper we investigate the effect of mediated partnerships over agents' equilibrium strategies in two-sided economic search. A mediated partnership is formed when an agent acts as a mediator, establishing a partnership between a pair of agents it encountered along its search, thereby reducing the other agents' amount of search. Surprisingly, this reduction in market friction induced by mediated partnerships does not always improve market efficiency. Use of mediated partnerships changes the equilibrium strategies used by agents in two-sided search models and introduces substantial computational complexity. This computational complexity is overcome with an innovative algorithm that facilitates equilibrium calculation. [ABSTRACT FROM AUTHOR]

In this paper we investigate methods for analyzing the expected value of adding information in distributed task scheduling problems. As scheduling problems are NP-complete, no polynomial algorithms exist for evaluating the impact a certain constraint, or relaxing the same constraint, will have on the global problem. We present a general approach where local agents can estimate their problem tightness, or how constrained their local subproblem is. This allows these agents to immediately identify many problems which are not constrained, and will not benefit from sending or receiving further information. Next, agents use traditional machine learning methods based on their specific local problem attributes to attempt to identify which of the constrained problems will most benefit from human attention. We evaluated this approach within a distributed cTAEMS scheduling domain and found this approach was overall quite effective. [ABSTRACT FROM AUTHOR]

In this paper we propose a multi-agent architecture, made of co-operative information agents, where agents can share with one another their knowledge of the environment and expertise in planning for achieving goals. In particular we consider how through communication such agents can incrementally learn partial and full plans. Such information exchange is particularly useful in the case of situated agents which have diverse abilities and expertise and which have partial views of their environments. It is also useful in the case of agent systems where agents collaborate towards achieving joint or individual goals. We describe an agent model based on abductive logic programming and give detailed protocols and policies of communication. We then define formally what it means for such information exchanges to be effective, and prove results regarding termination and effectiveness of dialogues based on the formalized policies. [ABSTRACT FROM AUTHOR]

Numerous CASE tools are used for applications analysis and design. These tools often do not take into account all the information (structures and constraints) given in a conceptual level. So, the elements obtained at the physic-cal level do not completely coincide with the conceptual elements. Consequent-ly, some semantics are lost. Our goal, in this paper, is to give rules to translate some constraints not taken into account in the processes used to translate the conceptual schema. In object databases, these constraints are expressed in OCL while they are expressed, in relational databases, using active mechanisms. Consequently, these constraints are checked during databases updates. [ABSTRACT FROM AUTHOR]

Knowing the geographical serving area of web resources is very important for many web applications. Here serving area stands for the geographical distribution of online users who are interested in a given web site. In this paper, we proposed a set of novel methods to detect the serving area of web resources by analyzing search engine logs. We use the search logs to detect serving area in two ways. First, we extracted the user IP locations to generate the geographical distribution of users who had the same interests in a web site. Second, query terms input by users were considered as the user knowledge about a web site. To increase the confidence and to cover new sites for use in real-time applications, we also proposed a categorization system for local web sites. A novel method for detecting the serving area was proposed based on categorizing the web content. For each category, a radius was assigned according to previous logs. In our experiments, we evaluated all these three algorithms. From the results, we found that the approach based on query terms was superior to that based on IP locations, since search queries for local sites tended to include location words while the IP locations were sometimes erroneous. The approach based on categorization was efficient for sites of known categories and were useful for small sites without sufficient number of query logs. [ABSTRACT FROM AUTHOR]

Problems with large monolithical ontologies in terms of reusability, scalability and maintenance have led to an increasing interest in modularization techniques for ontologies. Currently, existing work suffers from the fact that the notion of modularization is not as well understood in the context of ontologies as it is in software engineering. In this paper, we experiment on applying state-of-the-art tools for ontology modularization in the context of a concrete application: the automatic selection of knowledge components to be used for Web page annotation and semantic browsing. We conclude that, in a broader context, an evaluation framework is required to guide the choice of a modularization tool, in accordance with the requirements of the considered application. [ABSTRACT FROM AUTHOR]

Many GIS (Geographic Information Systems) handle composite geometries, i.e. geometries made of the union of simple shapes. Recent works show that relations between composite regions can be modelled with the well-known 9-Intersection Method (9IM). In this case, each relation is represented by a matrix. The proposed paper presents a general method to deduce the topological relations between the "parts" of regions from the matrix representation. Thus relations between composite regions could be easily implemented. [ABSTRACT FROM AUTHOR]

This paper presents a method to extract appearance descriptions for a given set of objects. Conversion between an object name and its appearance descriptions is useful for various applications, such as searching for an unknown object, memory recall support, and car/walk navigation. The method is based on text mining applied to Web search results. Using a manually constructed dictionary of visual modifiers, our system obtains a set of pairs of a visual modifier and a component/class for a given name of object, which best describe its appearance. The experimental results have demonstrated the effectiveness of our method in discovering appearance descriptions of various types of objects. [ABSTRACT FROM AUTHOR]

With the advances in web technologies, more and more information on the Web is contained in dynamically-generated web pages. Among several types of web "dynamism" the most important one is the case when web pages are generated as results of queries submitted via search web forms to databases available online. These pages constitute the portion of the Web known as deep Web. The existing estimates of the deep Web are predominantly based on study of English deep web sites. The key parameters of other-than-English segments of the deep Web were not investigated so far. Thus, currently known characteristics of the deep Web may be biased, especially owing to a steady increase in non-English web content. In this paper, we survey the part of the deep Web consisting of dynamic pages in one particular national domain. The estimation of the national deep Web is performed using the proposed sampling techniques. We report our observations and findings based on the experiments conducted in summer 2005. [ABSTRACT FROM AUTHOR]

K-anonymisation is an approach to protecting private information contained within a dataset. Many k-anonymisation methods have been proposed recently and one class of such methods are clustering-based. These methods are able to achieve high quality anonymisations and thus have a great application potential. However, existing clustering-based techniques use different quality measures and employ different data grouping strategies, and their comparative quality and performance are unclear. In this paper, we present and experimentally evaluate a family of clustering-based k-anonymisation algorithms in terms of data utility, privacy protection and processing efficiency. [ABSTRACT FROM AUTHOR]

Building models and learning patterns from a collection of data are essential tasks for decision making and dissemination of knowledge. One of the common tools to extract knowledge is to build a classifier. However, when the training dataset is sparse, it is difficult to build an accurate classifier. This is especially true in biological science, as biological data are hard to produce and error-prone. Through empirical results, this paper shows challenges in building an accurate classifier with a sparse biological training dataset. Our findings indicate the inadequacies in well known classification techniques. Although certain clustering techniques, such as seeded k-Means, show some promise, there are still spaces for further improvement. In addition, we propose a novel idea that could be used to produce more balanced classifier when training data samples are very limited. [ABSTRACT FROM AUTHOR]

Managing large amount of information on the internet needs more efficient and effective methods and techniques for mining and representing information. The use of ontologies for knowledge representation has had a fast increase in the last years: in fact the use of a common and formal representation of knowledge allows a more accurate analysis of a number of documents content, in several contexts. One of these challenging applications is the Web: the World Wide Web, in fact, has nowadays those kinds of requirements which are hard to satisfy, especially when one considers a complex scenario as the Semantic Web. In this paper we present a methodology for automatic topic annotation of Web pages. We describe an algorithm for words disambiguation using an apposite metric for measuring the semantic relatedness and we show a technique which allows to detect the topic of the analyzed document by means of ontologies extracted from a knowledge base. The strategy is implemented in a system where these information are taken into account to build a topic hierarchy automatically created and not a priori defined. Experimental results are presented and discussed in order to measure the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

With the growing popularity of distance courses the demand for good didactic materials also increases. However, preparing such material is a complicated task, often done in an inproper way, by simply transforming traditional teaching/learning material into a digital form. In such case, one of the most important aspects of learning - the presence of a teacher, is not considered. The following paper presents a method that allows creating didactic materials for distance learning that could compensate for the lack of direct contact with a teacher in the learning process. [ABSTRACT FROM AUTHOR]

Since a big part of web content is stored in relational databases (RDB) there are several approaches for generating of semantic web metadata from RDB. In our previous work we designed a novel approach for RDB to RDF data transformation. This paper describes experimental comparison of our system with several means of RDF production. We benchmarked both systems for the RDB to RDF transformation and native RDF repositories. The test results show a good performance of our system but also bring a new look at the effectiveness of the RDF production. [ABSTRACT FROM AUTHOR]

Finding all occurrences of a twig pattern in a database is a core operation in xml query processing. Recent study showed that tree-aware relational framework significantly outperform tree-unaware approaches in evaluating structural relationships in xml twig queries. In this paper, we present an efficient strategy to evaluate a specific class of structural relationship called nca-twiglet in a tree-unaware relational environment. Informally, nca-twiglet is a subtree in a twig pattern where all nodes have the same nearest common ancestor (the root of nca-twiglet). We focus on nca-twiglets having parent-child relationships. Our scheme is build on top of our Sucxent++ system. We show that by exploiting the encoding scheme of Sucxent++ we can reduce useless structural comparisons in order to evaluate nca-twiglets. Through a comprehensive experiment, we show that our approach is not only more scalable but also performs better than a representative tree-unaware approach on all benchmark queries with the highest observed gain factors being 352. [ABSTRACT FROM AUTHOR]

The notion of dependency is ubiquitous. This paper approaches this notion from the perspective of digital information preservation. At first, an abstract notion of module and dependency is introduced. Subsequently, and for building preservation information systems, the notion of profile is proposed as a gnomon for deciding representation information adequacy (during input) and intelligibility (during output). Subsequently some general dependency management services for identifying and filling gaps during input and output are described and analyzed (also described as protocols that could be used in the communication between a preservation information system and information consumers and providers). [ABSTRACT FROM AUTHOR]

In this paper we propose a practice for organizing modeling activity. We see substantial, successful modeling efforts in enterprises, e.g., in our logistics, manufacturing, banking and insurance projects, even without model-driven engineering metaphor. The focus of our discussion is the working domain expert. The working domain expert desires tool support, service support, and adaptivity of the modeling approach. We discuss these topics in the proven framework of the IT Infrastructure Library. [ABSTRACT FROM AUTHOR]

In this paper we provide a full-scale evaluation of a cluster-based architecture for P2P IR, focusing on retrieval effectiveness. We observe that there is a significant difference in performance between the architecture we examine and a centralised index. After inspecting our experimental methodology and our results, we provide evidence that suggests that this discrepancy is due to the information clustering algorithms employed throughout. The construction errors of the resource descriptions as well as the failure of the clustering mechanisms to discover the structure of the smallest of peer-collections lead to erroneous query routing. We proceed further to show experimentally how content replication and relevance-feedback mechanisms can help to alleviate the problem. [ABSTRACT FROM AUTHOR]

Emerging non-standard applications like the production of high-quality spatial sound pose new challenges to data management. Beside the need for a flexible transactional management of complex hierarchical scene descriptions a main requirement is the support of cooperative processes allowing a group of authors to edit a scene together in a distributed environment. Based on previous work on cooperative and non-standard transactions we present in this paper a transaction model and protocol for XML databases addressing this issues. [ABSTRACT FROM AUTHOR]

Due to the high frequency in location updates and the expensive cost of continuous query processing, server computation capacity and wireless communication bandwidth are the two limiting factors for large-scale deployment of moving object database systems. Many techniques have been proposed to address the server bottleneck including one using distributed servers. To address both of the scalability factors, P2P computing has been considered. These schemes enable moving objects to participate as a peer in query processing to substantially reduce the demand on server computation, and wireless communications associated with location updates. Most of these techniques, however, assume an open-space environment. In this paper, we investigate a P2P computing technique for continuous kNN queries in a network environment. Since network distance is different from Euclidean distance, techniques designed specifically for an open space cannot be easily adapted for our environment. We present the details of the proposed technique, and discuss our simulation study. The performance results indicate that this technique can significantly reduce server workload and wireless communication costs. [ABSTRACT FROM AUTHOR]

The Rate-Based Progressive Join (RPJ) is a non-blocking relational equijoin algorithm. It is an equijoin that can deliver results progressively. In this paper, we first present a naive extension, called neRPJ, to the progressive computation of the similarity join of high-dimensional data. We argue that this naive extension is not suitable. We therefore propose an adequate solution in the form of a Result-Rate Progressive Join (RRPJ) for high-dimensional distance similarity joins. Using both synthetic and real-life datasets, we empirically show that RRPJ is effective and efficient, and outperforms the naive extension. [ABSTRACT FROM AUTHOR]

Prevailing concurrency control mechanisms for multidimensional index structures, such as the Generalized Search Tree (GiST), are based on locking techniques. These approaches may cause significant overhead in settings where the indexed data is rarely updated and read access is highly concurrent. In this paper we present the Multiversion-GiST (MVGiST), which extends the GiST with Multiversion Concurrency Control. Beyond enabling lock-free read access, our approach provides readers a consistent view of the whole index structure, which is achieved through the creation of lightweight, read-only versions of the GiST that share unchanging nodes amongst themselves. Our evaluation confirms that for low update rates, the MVGiST significantly improves scalability w.r.t. the number of concurrent accesses when compared to a traditional, locking-based concurrency control mechanism. [ABSTRACT FROM AUTHOR]

The most serious challenges Personal Information Management Systems face today are the results of having to deal with a large number of heterogeneous types of data from diverse data sources, but having no means of managing and searching them in a convenient, unified fashion. We argue that simplicity and flexibility are essential attributes for the next-generation search tools to respond to these challenges. This paper lays out specific issues to realizing such a tool in the context of desktop search and ties them to existing search techniques employed by Database Management Systems and Information Retrieval - the two leading disciplines in search technology. We propose a novel technique for desktop search and show how our combined database and information retrieval approach to searching heterogeneous desktop data is going to benefit a large community of users. [ABSTRACT FROM AUTHOR]

The labeling problem of dynamic XML documents has received increasing research attention. When XML documents are subject to insertions and deletions of nodes, it is important to design a labeling scheme that efficiently facilitates updates as well as processing of XML queries. This paper proposes a novel encoding scheme, vector encoding which is orthogonal to existing labeling schemes and can completely avoid re-labeling. Extensive experiments show that our vector encoding outperforms existing labeling schemes on both label updates and query processing especially in the case of skewed updates. Besides, it has the nice property of being conceptually easy to understand through its graphical representation. [ABSTRACT FROM AUTHOR]

It is important to process the updates when nodes are inserted into or deleted from the XML tree. However, all the existing labeling schemes have high update cost. In this paper, we innovatively introduce a concept of Forbidden Code Segment (FCS), and then propose a novel and efficient encoding approach, called Extended Lexicographical Order encoding based on Forbidden Code Segment (FCS-ELO Encoding), whose codes are more compact than CDBS and QED codes. The most important characteristic is that our FCS-ELO labeling scheme can gracefully handle arbitrary update patterns and completely avoid re-labeling in XML updates, which is not at the sacrifice of query performance. We deliver the detailed theoretic analyses and experiments to show that, the proposed labeling scheme is superior to all the existing dynamic labeling schemes to process updates in terms of the incremental label size and the time for updating. [ABSTRACT FROM AUTHOR]

This paper proposes a flexible caching solution to improve query evaluation in grids. It reduces both, data transfer and query computation, by adopting a distributed semantic caching approach. Our proposal introduces multi-scale cache cooperation including single site cooperation between object caches and distributed context aware cooperation between several query caches. Different cache miss resolution protocols are introduced for query evaluation and experimented in a grid data management for bioinformatics applications. [ABSTRACT FROM AUTHOR]

Combining outputs of multiple classifiers is one of most important techniques for improving classification accuracy. In this paper, we present a new classifier ensemble based on artificial immune recognition system (AIRS) classifier and independent component analysis (ICA) for detecting the cardiac disorders from SPECT images. Firstly, the dimension of SPECT (Single Photon Emission Computed Tomography) images dataset, which has 22 binary features, was reduced to 3, 4, and 5 features using FastICA algorithm. Three different feature subsets were obtained in this way. Secondly, the obtained feature subsets were classified by AIRS classifier and then stored the outputs obtained from AIRS classifier into the result matrix. The exact result that denote whether subject has cardiac disorder or not was obtained by averaging the outputs obtained from AIRS classifier into the result matrix. While only AIRS classifier obtained 84.96% classification accuracy with 50-50% train-test split for diagnosing the cardiac disorder from SPECT images, classifier ensemble based on AIRS and ICA fusion obtained 97.74% classification accuracy on the same conditions. The accuracy of AIRS classifier utilizing the reduced feature subsets was higher than those exploiting all the original features. These results show that the proposed ensemble method is very promising in diagnosis of the cardiac disorder from SPECT images. [ABSTRACT FROM AUTHOR]

Decision tables are a widely used knowledge management tool in the decision making process. Ambiguity and conceptual reasoning difficulties arise while designing large decision tables in a collaborative environment. We introduce the notion of Semantic Decision Table (SDT), which enhances a decision table with explicit decision semantics by annotating it properly with a domain ontology. In this paper, we focus on the SDT construction process. First, we map decision items to the ontology by building a rooted tree of decisionbinary facts and visualize it in a scalable manner. Formal ontological roles are used during this mapping process. Then, we commit the decision rules to the mapping results with a high level pseudo-natural language to ground their semantic. We illustrate with an SDT example from the domain of human resource management. [ABSTRACT FROM AUTHOR]

The problem of storing and querying XML data using relational databases has been considered a lot and many techniques have been developed. MXML is an extension of XML suitable for representing data that assume different facets, having different value and structure under different contexts, which are determined by assigning values to a number of dimensions. In this paper, we explore techniques for storing MXML documents in relational databases, based on techniques previously proposed for conventional XML documents. Essential characteristics of the proposed techniques are the capabilities a) to reconstruct the original MXML document from its relational representation and b) to express MXML context-aware queries in SQL. [ABSTRACT FROM AUTHOR]

In this paper we describe how to mine association rules in temporal document collections. We describe how to perform the various steps in the temporal text mining process, including data cleaning, text refinement, temporal association rule mining and rule post-processing. We also describe the Temporal Text Mining Testbench, which is a user-friendly and versatile tool for performing temporal text mining, and some results from using this tool. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of clustering textual units in the framework of helping an expert to build a specialized ontology. This work has been achieved in the context of a French project, called Biotim, handling botany corpora. Building an ontology, either automatically or semi-automatically is a difficult task. We focus on one of the main steps of that process, namely structuring the textual units occurring in the texts into classes, likely to represent concepts of the domain. The approach that we propose relies on the definition of a new non-symmetrical measure for evaluating the semantic proximity between lemma, taking into account the contexts in which they occur in the documents. Moreover, we present a non-supervised classification algorithm designed for the task at hand and that kind of data. The first experiments performed on botanical data have given relevant results. [ABSTRACT FROM AUTHOR]

This paper considers the problem of reasoning on massive amounts of (possibly distributed) data. Presently, existing proposals show some limitations: (i) the quantity of data that can be handled contemporarily is limited, due to the fact that reasoning is generally carried out in main memory; (ii) the interaction with external (and independent) DBMSs is not trivial and, in several cases, not allowed at all; (iii) the efficiency of present implementations is still not sufficient for their utilization in complex reasoning tasks involving massive amounts of data. This paper provides a contribution in this setting; it presents a new system, called DLVDB, which aims to solve all these problems. [ABSTRACT FROM AUTHOR]

This paper presents Dependency Tree Semantics (DTS), an underspecified logic for representing quantifier scope ambiguities. DTS features a direct interface with a Dependency grammar, an easy management of partial disambiguations and the ability to represent branching quantifier readings. This paper focuses on the syntax of DTS, while does not take into account the model-theoretic interpretation of its well-formed structuresWe address the reader to [15], in which a possible semantics for DTS is outlined in terms of a comparison with the logics developed by [21] and [22] for dealing with branching quantification and generalized quantifiers.. [ABSTRACT FROM AUTHOR]

In this paper we investigate the use of SAT technology for solving constraint problems. In particular, we solve many instances of several common benchmark problems for CP with different SAT solvers, by exploiting the declarative modelling language NPSpec, and Spec2Sat, an application that allows us to compile NPSpec specifications into SAT instances. Furthermore, we start investigating whether some reformulation techniques already used in CP are effective when using SAT as solving engine. We present encouraging experimental results in this direction, showing that this approach can be appealing. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the influence of the indiscernibility degree, which is a primary parameter in rough clustering, on cluster formation. Rough clustering consists of two steps: (1)assignment of initial equivalence relations and (2)iterative refinement of the initial relations. Indiscernibility degree plays a key role in the second step, but it is not easy to independently analyze its characteristics because it inherits the results of step 1. In this paper, we employ the perfect initial equivalence relations, which were generated according to class labels of data, to seclude the influence of step 1. We first examine the relationship between the threshold value of indiscernibility degree and resultant clusters. After that, we apply random disturbance to the perfect relations, and examine how the result changes. The results demonstrated that the relationships between indiscernibility degree and the number of clusters draw a globally convex but multi-modal curve, and the range of indiscernibility degree that yields best cluster validity may exist on a local minimum around the global one which generates single cluster. Keywords: Indiscernibility, Clustering, Rough Set. [ABSTRACT FROM AUTHOR]

Graph coloring is used to characterize some properties of graphs. A b-coloring of a graph G (using colors 1,2,...,k) is a coloring of the vertices of G such that (i) two neighbors have different colors (proper coloring) and (ii) for each color class there exists a dominating vertex which is adjacent to all other k-1 color classes. In this paper, based on a b-coloring of a graph, we propose a new clustering technique. Additionally, we provide a cluster validation algorithm. This algorithm aims at finding the optimal number of clusters by evaluating the property of color dominating vertex. We adopt this clustering technique for discovering a new typology of hospital stays in the French healthcare system. [ABSTRACT FROM AUTHOR]

Despite many recent developments, there are still a number of central issues in inductive databases that need more research. In this paper we address two of them. The first issue is about how the discovery of patterns can use existing patterns. We will give a concrete example showing an advantage of mining both the patterns and the data. The second issue we consider is the actual implementation of inductive databases. We will propose an architectural framework for inductive databases and show how existing databases can be incorporated. [ABSTRACT FROM AUTHOR]

This paper addresses fuzzy weighted multi-cross-level association rule mining. We define a fuzzy data cube, which facilitates for handling quantitative values of dimensional attributes, and hence allows for mining fuzzy association rules at different levels. A method is introduced for single dimension fuzzy weighted association rules mining. To the best of our knowledge, none of the studies described in the literature considers weighting the internal nodes in such taxonomy. Only items appearing in transactions are weighted to find more specific and important knowledge. But, sometimes weighting internal nodes on a tree may be more meaningful and enough. We compared the proposed approach to an existing approach that does not utilize fuzziness. The reported experimental results demonstrate the effectiveness and applicability of the proposed fuzzy weighted multi-cross-level mining approach. [ABSTRACT FROM AUTHOR]