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Big Data Clustering

XPath expressions define navigational queries on XML data and are issued on XML documents to select sets of element nodes. Due to the wide use of XPath, which is embedded into several languages for querying and manipulating XML data, the problem of efficiently answering XPath queries has received increasing attention from the research community. As the efficiency of computing the answer of an XPath query depends on its size, replacing XPath expressions with equivalent ones having the smallest size is a crucial issue in this direction. This article investigates the minimization problem for a wide fragment of XPath (namely X P [✶] ), where the use of the most common operators (child, descendant, wildcard and branching) is allowed with some syntactic restrictions. The examined fragment consists of expressions which have not been specifically studied in the relational setting before: neither are they mere conjunctive queries (as the combination of “//” and “*” enables an implicit form of disjunction to be expressed) nor do they coincide with disjunctive ones (as the latter are more expressive). Three main contributions are provided. The “global minimality” property is shown to hold: the minimization of a given XPath expression can be accomplished by removing pieces of the expression, without having to re-formulate it (as for “general” disjunctive queries). Then, the complexity of the minimization problem is characterized, showing that it is the same as the containment problem. Finally, specific forms of XPath expressions are identified, which can be minimized in polynomial time.

The pervasive diffusion of new generation devices like smart phones and tablets along with the widespread use of social networks causes the generation of massive data flows containing heterogeneous information generated at different rates and having different formats. These data are referred as \emph{Big Data} and require new storage and analysis approaches to be investigated for managing them. In this paper we will describe a system for dealing with massive tourism flows that we exploited for the analysis of tourist behavior in Italy. We defined a framework that exploits a NoSQL approach for data management and map reduce for improving the analysis of the data gathered from different sources

This paper presents a formal definition of an extension of the XQuery Full-Text language: The proposed extension consists in adding two new flexible axes, named below and near, which express structural constraints that can be specified by the user. Both constraints are evaluated in an approximate way with respect to a considered path, and their evaluation produces a path relevance score for each retrieved element. The formal syntax and the semantics of the two new axis are presented and discussed.

Malware Detection

Due to the emerging Big Data paradigm traditional data management techniques result inadequate in many real life scenarios. In particular, OLAP techniques require substantial changes in order to offer useful analysis due to huge amount of data to be analyzed and their velocity and variety. In this paper, we describe an approach for dynamic Big Data searching that based on data collected by a suitable storage system, enriches data in order to guide users through data exploration in an efficient and effective way.

In this paper we present a new technique for detecting changes on the Web. We propose a new method to measure the similarity of two documents, that can be efficiently used to discover changes in selected portions of the original document. The proposed technique has been implemented in the CDWeb system providing a change monitoring service on theWeb. CDWeb differs from other previously proposed systems since it allows the detection of changes on portions of documents and specific changes expressed by means of complex conditions, i.e. users might want to know if the value of a given stock has increased by more than 10%. Several tests on stock exchange and auction web pages proved the effectiveness of the proposed approach.

Radio Frequency Identification (RFID) applications are emerging as key components in object tracking and supply chain management systems since in the next future almost every major retailer will use RFID systems to track the shipment of products from suppliers to warehouses. Due to the streaming nature of RFID readings, large amounts of data are generated by these devices at high production rates. This phenomenon is even more relevant since RFIDs are so cheap that every individual item can be tagged thus leaving a "trail" of data as it moves across different locations. This scenario raises new challenges in effectively and efficiently exploiting such large amounts of data. In this paper we address the problem of compressing RFID data in order to enable devices with limited amount of available memory (such as PDAs) to issue queries on RFID warehouses. In particular, we designed a lossy strategy for collapsing tuples carrying information about items being delivered at different location of the supply chain.

International audience; Biclustering is similar to formal concept analysis (FCA), whose objective is to retrieve all maximal rectangles in a binary matrix and arrange them in a concept lattice. FCA is generalized to more complex data using pattern structure. In this article, we explore the relation of biclustering and pattern structure. More precisely, we study the order-preserving biclusters, whose rows induce the same linear order across all columns.

Nowadays, multimedia data is surely one of the most popular and pervasive information and communication media that accompanies us in almost every walk of lives. They allow fast and effective communication and sharing of information about peoples’ lives, their behaviors, works, interests, and they are also the digital testimony of facts, objects, and locations and have become an essential component of social media networks. Technically speaking, how to organize and structure this huge amount of data using different paradigms, so that we can easily get useful information, has been a challenging research field for decades. In this chapter we will describe the main results produced by the Multimedia Database Research Group of University of Naples in this area: models for representing multimedia data and the related knowledge and techniques for their storage, indexing and retrieval. In addition, we also point out several applications, with a particular emphasis on social media networks.