Related papers: Materialized View Selection by Query Clustering in…

Today's database is associated with interoperability between different domains and applications. This consequently results in the importance of data portability in database. XML format fits the requirements and it has been increasingly used…

Multi-view data clustering refers to categorizing a data set by making good use of related information from multiple representations of the data. It becomes important nowadays because more and more data can be collected in a variety of…

In this work, we introduce and study the novel task of Open-ended Semantic Multiple Clustering (OpenSMC). Given a large, unstructured image collection, the goal is to automatically discover several, diverse semantic clustering criteria…

In the era of big data, it is common to have data with multiple modalities or coming from multiple sources, known as "multi-view data". Multi-view clustering provides a natural way to generate clusters from such data. Since different views…

W3C's XML-Query language offers a powerful instrument for information retrieval on XML repositories. This article describes an implementation of this retrieval in a real world's scenario. Distributed XML-Query processing reduces load on…

XML has emerged as the leading language for representing and exchanging data not only on the Web, but also in general in the enterprise. XQuery is emerging as the standard query language for XML. Thus, tools are required to mediate between…

Multiple clustering aims at exploring alternative clusterings to organize the data into meaningful groups from different perspectives. Existing multiple clustering algorithms are designed for single-view data. We assume that the…

Materialized views (MVs), stored pre-computed results, are widely used to facilitate fast queries on large datasets. When new records arrive at a high rate, it is infeasible to continuously update (maintain) MVs and a common solution is to…

Over the past ten years, many different approaches have been proposed for different aspects of the problem of resources management for long running, dynamic and diverse workloads such as processing query streams or distributed deep…

Multi-view clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made recently, most existing methods undergo high complexity,…

With the need for flexible and on-demand decision support, Dynamic Data Warehouses (DDW) provide benefits over traditional data warehouses due to their dynamic characteristics in structuring and access mechanism. A DDW is a data framework…

Interoperability of potentially heterogeneous databases has been an ongoing research issue for a number of years in the database community. With the trend towards globalization of data location and data access and the consequent requirement…

The need for discovering knowledge from XML documents according to both structure and content features has become challenging, due to the increase in application contexts for which handling both structure and content information in XML data…

With the emergence of XML as de facto format for storing and exchanging information over the Internet, the search for ever more innovative and effective techniques for their querying is a major and current concern of the XML database…

Self-supervised features are the cornerstone of modern machine learning systems. They are typically pre-trained on data collections whose construction and curation typically require extensive human effort. This manual process has some…

In recent years, the significant growth of RDF data used in numerous applications has made its efficient and scalable manipulation an important issue. In this paper, we present RDFViewS, a system capable of choosing the most suitable views…

In this article, we describe the XML storage system used in the WebContent project. We begin by advocating the use of an XML database in order to store WebContent documents, and we present two different ways of storing and querying these…

With the ever-growing availability of so-called complex data, especially on the Web, decision-support systems such as data warehouses must store and process data that are not only numerical or symbolic. Warehousing and analyzing such data…

The quality of machine learning models depends heavily on their training data. Selecting high-quality, diverse training sets for large language models (LLMs) is a difficult task, due to the lack of cheap and reliable quality metrics. While…

We consider the setting of a Semantic Web database, containing both explicit data encoded in RDF triples, and implicit data, implied by the RDF semantics. Based on a query workload, we address the problem of selecting a set of views to be…