1,721,068 research outputs found
Rewriting Rules to Permeate Complex Similarity and Fuzzy Queries within a Relational Database System
No full textIn recent years, the availability of complex data repositories
(e.g. multimedia, genomic, semistructured databases) has paved
the way to new potentials as to data querying. In this scenario,
similarity and fuzzy techniques have proved to be successful
principles for effective data retrieval. However, most proposals
are domain specific and lack of a general and integrated approach
to deal with generalized complex queries, i.e. queries
where multiple conditions are expressed, possibly on complex as
well as on traditional data. To overcome such limitations, much
work has been devoted to the development of middleware systems to support query processing on multiple repositories.
On a similar line, in this paper we present a formal framework to
permeate complex similarity and fuzzy queries within a
relational database system. As an example, we focus on multimedia
data, which is represented in an integrated view with common
database data. We have designed an application layer that relies
on an algebraic query language, extended with MM-tailored
operators, and that maps complex similarity and fuzzy queries to
standard SQL statements that can be processed by a relational
database system, exploiting standard facilities of modern
extensible RDBMS.
To show the applicability of our proposal, we implemented a prototype that provides the user with rich query capabilities, ranging from traditional database queries, to complex queries gathering a mixture of Boolean, similarity and fuzzy predicates on the data
Efficiently Answering Personalized Queries on XML Data
No full textThe semantic and structural heterogeneity of large XML digital libraries emphasizes the need of supporting approximate queries, i.e. queries where the matching conditions are relaxed so as to retrieve results that possibly partially satisfy the user’s requests. The paper proposes a flexible query answering framework which efficiently supports complex approximate queries on XML data.
To reduce the number of relaxations applicable to a query, the paper relies on the specification of user preferences about the types of approximations allowed. A specifically devised index structure which efficiently supports both semantic and structural approximations, according to the specified user preferences, is proposed. Also, a ranking model to quantify approximations in the results is presented.
Personalized queries, on one hand, effectively narrow the space of query reformulations, on the other hand, enhance the user query capabilities with a great deal of flexibility and control over requests. As to the quality of results, the retrieval process considerably benefits of the presence of user preferences in the queries. Experiments demonstrate the effectiveness and the efficiency of the proposal, as well as its scalability
Approximating expressive queries on graph-modeled data: The GeX approach
Full text linkWe present the GeX (Graph-eXplorer) approach for the approximate matching of complex queries on graph-modeled data. GeX generalizes existing approaches and provides for a highly expressive graph-based query language that supports queries ranging from keyword-based to structured ones. The GeX query answering model gracefully blends label approximation with structural relaxation, under the primary objective of delivering meaningfully approximated results only. GeX implements ad-hoc data structures that are exploited by a top-k retrieval algorithm which enhances the approximate matching of complex queries. An extensive experimental evaluation on real world datasets demonstrates the efficiency of the GeX query answering
Journal of Computer and System Sciences Special Issue on Query Answering on Graph-Structured Data
No full textGraph-based data models have recently gained much popularity as powerful means for data representation in several database application areas. Notable examples of application domains where data is naturally represented in graph-based form are knowledge bases, biological and chemical databases, Web-scattered data, healthcare, personal information management (PIM), enterprise information management (EIM) systems, online mapping/routing services, and social networks, just to mention a few. The heterogeneity, complexity and largeness of contents that characterize datasets in these fields unquestionably make the querying experience a really challenging task.
This special issue of the Journal of Computer and System Sciences follows the 2013 and 2014 editions of the International Workshop on Querying Graph Structured Data (GraphQ), which were co-located with the International Conference on Extending Database Technology and were held in Genoa, Italy and in Athens, Greece, respectively. The two editions of the workshop attracted a large world-wide audience of researchers and professionals, and yielded several excellent presentations exploring how to effectively and efficiently support graph queries in different application domains. This special issue includes a shortlist of selected contributions that were extended to provide deeper investigations along three main research directions: (1) graph query answering; (2) graph query processing; (3) graph data dynamics
Semantic Web Service Composition in the NeP4B Project: Challenges and Architectural Issues
No full textSemantic Web service discovery and composition frameworks proposed so far assume for the most part a centralized registry that holds information of all the Web services available at any given time. This solution does not well cope with the scalability and flexibility requirements of dynamic, fast changing contexts. As part of the NeP4B project, in this paper we propose an alternative peer to peer architecture based on the Goal concept
A Data Management Middleware for ITS Services in Smart Cities
Full text linkA major societal challenge to be tackled in megacities is sustainable urban transportation. Intelligent Transportation Systems (ITSs) are actually data-centric applications that need to store and query real-time as well as historical/static data from various data sources and have to provide timely responses to users' transportation needs.
In this paper we introduce a data management middleware that offers the robustness of a common framework to support the development of smart applications having the above needs. It supports the efficient storage and access to real-time and historical/static data and provides both one-time and continuous query capabilities. While the middleware has been designed to be general and versatile to support data management for any kind of application, in this paper we explore its suitability to ITS smart services also by means of an experimental evaluation conducted on a variety of traffic scenarios
SRI@work: Efficient and Effective Routing Strategies in a PDMS
No full textIn recent years, information sharing has gained much benefit by the large diffusion of distributed computing, namely through P2P systems and, in line with the Semantic Web vision, through Peer Data Management Systems (PDMSs). In a PDMS scenario one of the most difficult challenges is query routing, i.e. the capability of selecting small subsets of semantically relevant peers to forward a query to.
In this paper, we put the Semantic Routing Index (SRI) distributed mechanism we proposed in [6] at work. In particular, we present general SRI-based query execution models, designed around different performance priorities and minimizing the information spanning over the network. Starting from these models, we devise several SRI-enabled routing policies, characterized by different effectiveness and efficiency targets, and we deeply test them in ad-hoc PDMS simulation environments
Streaming Tables: Native Support to Streaming Data in DBMSs
Full text linkData stream management systems (DSMSs) are conceived for running continuous queries (CQs) on the most recently streamed data. This model does not completely fit the needs of several modern data-intensive applications that require to manage recent/historical/static data and execute both CQs and OTQs joining such data. In order to cope with these new needs, some DSMSs have moved toward the integration of database management systems (DBMSs) functionalities to augment their capabilities. In this paper we adopt the opposite perspective and we lay the groundwork for extending DBMSs to natively support streaming facilities. To this end, we introduce a new kind of table, the streaming table, as a persistent structure where streaming data enters and remains stored for a long period, ideally forever. Streaming tables feature a novel access paradigm: continuous writes and one-time as well as continuous reads. We present a streaming table implementation and two novel types of indices that efficiently support both update and scan high rates. A detailed experimental evaluation shows the effectiveness of the proposed technology
Data-Sharing P2P Networks with Semantic Approximation Capabilities
No full textThe synergy between Peer-to-Peer systems and Semantic Web technologies has paved the way for large-scale sharing of semantically rich data, usually represented through schemas like, for instance, RDF or ontologies.Because of the lack of common understanding of the vocabulary used by peers, the resulting heterogeneity of data representations opens new challenges as to the efficient and effective retrieval of relevant information.In this paper, as opposed to viewing semantic misalignment as a limit for interoperability, we leverage on the presence of semantic approximations between the peers' schemas as a means for giving effective hints along two directions: 1) for query routing purposes, to identify the peers which best satisfy the user's requests, and 2) for making users aware of the relevance of the returned answers through a ranking mechanism which promotes the most semantically related results
Unleashing the power of querying streaming data in a temporal database world: A relational algebra approach
Full text linkModern data-intensive applications have to manage huge quantities of streaming/relational data and need advanced query capabilities involving combinations of continuous queries (CQs) and one-time queries (OTQs) also requiring the verification of complex temporal conditions. In this paper, we go beyond the disjointed panorama of current approaches and adopt a new holistic approach to the integration of stream processing capabilities into the temporal database world based on the streaming table concept. To this end, we propose a full-fledged query interface composed of a TSQL2-like query language with an underlying algebraic framework. The algebraic framework, which is aimed at implementing the query interface on top of a working DBMS, is made up of: (a) the extended temporal algebra TA⋆ supporting OTQs with an hybrid temporal semantics (sequenced and non-sequenced); (b) the continuous temporal algebra CTA that extends TA⋆ with window expressions for CQ specification; (c) the translation of CTA expressions into TA⋆ ones that can be executed by a traditional DBMS with an extended kernel
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