Anna Maddalena

Accelerating queries by pruning XML documents

Some XML query processors operate on an internal representation of XML documents and can leverage neither the XML storage structure nor the possible access methods dedicated to this storage structure. Such query processors are often used in organizations that usually process transient XML documents received from other organizations. In this paper, we propose a different approach to accelerating query execution on XML source documents in such environments. The approach is based on the notion of query equivalence of XML documents with respect to a query. Under this equivalence, we propose two different document transformation strategies which prune parts of the documents irrelevant to the query, just before executing the query itself. The proposed transformations are implemented and evaluated using a two-level index structure: a structural directory capturing document paths and an inverted index of tag offsets.

Modeling and language support for the management of pattern-bases

Information overloading is today a serious concern that may hinder the potential of modern web-based information systems. A promising approach to deal with this problem is represented by knowledge extraction methods able to produce artifacts (also called patterns) that concisely represent data. Patterns are usually quite heterogeneous and voluminous. So far, little emphasis has been posed on developing an overall integrated environment for uniformly representing and querying different types of patterns. In this paper we consider the larger problem of modeling, storing, and querying patterns, in a database-like setting and use a Pattern-Base Management System (PBMS) for this purpose. Specifically, (a) we formally define the logical foundations for the global setting of pattern management through a model that covers data, patterns, and their intermediate mappings; (b) we present a formalism for pattern specification along with safety restrictions; and (c) we introduce predicates for comparing patterns and query operators.

A framework for data mining pattern management

To represent and manage data mining patterns, several aspects have to be taken into account: (i) patterns are heterogeneous in nature; (ii) patterns can be extracted from raw data by using data mining tools (a-posteriori patterns) but also defined by the users and used for example to check how well they represent some input data source (a-priori patterns); (iii) since source data change frequently, issues concerning pattern validity and synchronization are very important; (iv) patterns have to be manipulated and queried according to specific languages. Several approaches have been proposed so far to deal with patterns, however all of them lack some of the previous characteristics. The aim of this paper is to present an overall framework to cope with all these features.

A clustering approach for XML linked documents

Clustering algorithms for hypertext documents consider not only the document content but also the links existing between them. All the similarity functions proposed in the literature assume that just one type of link exists between documents, with a unique semantic meaning. With the rapid diffusion of XML documents, a specific language, called XLink, has been proposed to specify inside XML documents different types of links. Each type of link forces a different degree of similarity between the documents on which it is defined, thus we claim it must influence in a different way the computation of distance values. In this paper, after presenting a graph-based formalization of the hypertexts we consider, we introduce a distance function, based on both the number and the type of the links connecting documents. Some preliminary experimental results on clustering algorithms based on the proposed function conclude the paper.

Pattern based management: data models and architectural aspects

Patterns are concise, but rich in semantic, representation of data (data mining results, multimedia features, Web structural information, etc.) Only few approaches exist to deal with heterogeneous patterns, even if this is an hot topic in most data-intensive and distributed applications The aim of this thesis is to investigate the potentiality of pattern-based management In particular, we plan to define sufficiently expressive pattern data models and languages to cope with different types of patterns Besides, investigation of architectural issues concerning the usage of the proposed model and languages in centralized and advanced distributed environments will be exploited.

Flexible pattern management within PSYCHO

Patterns are concise, but rich in semantic, representation of data. The approaches proposed in the literature and by commercial systems for pattern management usually deal with few types of knowledge artifacts and mainly concern pattern extraction issues. Little effort has been posed in designing an overall framework dedicated to the management of different types of patterns, possibly user-defined, in an homogeneous way. PSYCHO (Pattern based SYstem arCHitecture prOtotype) is a recently developed tool, built on top of Oracle technologies, for generating, representing, and manipulating heterogeneous patterns, possibly user-defined. The aim of this paper is to present the PSYCHO system, by discussing the underlying theory, the reference architecture, and providing concrete examples of its usage.

A framework for cluster management

To represent and manipulate data extracted from Web-server logs or applicational logs, clustering techniques can be used The generated clusters are often different in types, are generated by using different algorithms, and should be homogeneously manipulated together with other knowledge mined from data, for example association rules or decision trees The problem thus arises of using an homogeneous framework in which cluster results can be represented and manipulated, possibly together with other data mining results In this paper, we show how the pattern modeling framework presented in [5,10] can be used to this purpose.

Prune XML before you search it: XML transformations for query optimization

In this paper, we present a query optimization approach for XML document management environments loosely-coupled with the storage system Our technique is based on two main steps: first, based on the query, input documents are transformed, by pruning parts that are irrelevant with respect to the query; then the query is executed on the pruned documents An index structure is also provided to further optimize the pruning process Experimental results show that, by using our pruning strategy, query execution time can be significantly reduced.