Combining Description Logics with synopses for inferring complex knowledge patterns from XML sources

Class-centered data models, such as the object-oriented data model, are inadequate for supporting engineering design product models because of their lack of support for object evolution, schema evolution, and semantic and user-defined relationships. Description logic overcomes these limitations by providing constructs for intentional description of classes, relationships, and objects. By combining description logic with object-oriented modelling concepts, design product schemas and data can be uniformly represented and modified throughout the design process.

Handling the interoperability issues in multiple, heterogeneous XML sources is central in XML data management and mining. In this chapter, we present a framework for the intensional integration and exploration of XML sources. Specifically, we propose a three-layer framework aimed at extracting interschema knowledge from the available sources, constructing a hierarchy based on the extracted knowledge to represent the sources at different abstraction levels, and finally organizing and exploring the sources through the constructed hierarchy. We also describe possible implementations of each of the three layers, focusing on the extraction of intensional interschema properties, the intensional integration of XML sources, and the clustering of XML schemas. In order to better handle the complexity of its activities, the proposed framework has been designed by means of the layers architecture patterns and the component-based development paradigm.

This chapter describes a framework for querying heterogeneous XML data sources, that extends previous approaches for approximate query evaluation, by providing techniques for combining partial answers coming from different sources. This approach does not rely on a global schema shared by the sources, but it automatically adapts the query to the available data, providing the user with the XML elements satisfying the query to a certain extent. Based on this framework, a query language is described which allows the collection of as much information as possible from several heterogeneous XML sources. An algorithm for approximately evaluating a query on a single source and a strategy to join partial results coming from different sources are provided. Finally, an experimental validation of the approach in a peer-to-peer application scenario is presented.

In this paper, we consider the expressive Description Logic (DL) $$\mathcal{ALCQPI}_{R^{+}}$$ having concept constructors called part restrictions that realize rational grading. Part restrictions are able to convey statements about a rational part of a set of successors, and they essentially enrich the expressive capabilities of DLs. That is why it is important to evaluate the cost of their use with respect to the reasoning complexity. We modify the tableaux algorithm used for $$\mathcal{ALCNI}_{R^{+}}$$ (a DL with just number restrictions instead of qualified number ones), and augment it with a specific technique for dealing with part restrictions to prove that concept satisfiability and subsumption in the considered DL are still in PSPACE.

This chapter introduces different alternatives to store and manage jointly relational and eXtensible Markup Language (XML) data sources. Nowadays, businesses are transformed in e business and have to manage large data volumes and from heterogeneous sources. To manage large amounts of information, Database Management Systems (DBMS) continue to be one of the most used tools, and the most extended model is the relational one. On the other side, XML has reached the de facto standard to present and exchange information between businesses on the Web. Therefore, it could be necessary to use tools as mediators to integrate these two different data to a common format like XML, since it is the main data format on the Web. First, a classification of the main tools and systems where this problem is handled is made, with their advantages and disadvantages. The objective will be to propose a new system to solve the integration business information problem.Request access from your librarian to read this chapter's full text.

Conducting bioinformatic analyses involves biologists in expressing requests over a range of highly heterogeneous information sources and software tools. Such activities are laborious, and require detailed knowledge of the data structures and call interfaces of the different sources. The TAMBIS (Transparent Access to Multiple Bioinformatics Information Sources) project seeks to make the diversity in data structures, call interfaces and locations of bioinformatics sources transparent to users. In TAMBIS, queries are expressed in terms of an ontology implemented using a description logic, and queries over the ontology are rewritten to a middleware level for execution over the diverse sources. The paper describes query processing in TAMBIS, focusing in particular on the way source-independent concepts in the ontology are related to source-dependent middleware calls, and describing how the planner identifies efficient ways of evaluating user queries.

Semantic operations of multiple soft sets under conflict

There are many techniques for specifying software. However, they are not usually presented in a way that is directly applicable to object-oriented software. This tutorial surveys both operational and descriptive specification techniques, shows how to use them in several popular object-oriented software engineering methods, and provides case studies of developing useful, formal specification mechanisms that are appropriate for object-oriented software engineering.

This paper provides different kinds of techniques for lossless data compression and comparison between them. By eliminating redundant bits, data compression decreases the file size. In order to reduce the capacity needed for that data, it decreases the redundant bits in data representation and thus uses the bandwidth effectively to reduce the communication cost. Compression of data saves file volume, network bandwidth and speeds up the transfer speed as well. Lossless and Lossy are the two techniques for data compression. Lossless compression maintains the data properly.

Data compression techniques in IoT-enabled wireless body sensor networks: A systematic literature review and research trends for QoS improvement

The aim of this paper is to illustrate how a probabilistic Description Logics, called DL P, can be exploited for reasoning about information sources characterized by heterogeneous formats and structures. The paper first introduces DL P syntax and semantics. Then, a DL P-based approach is illustrated for inferring complex knowledge assertions from information sources characterized by heterogeneities in formats and representational structures. The thus obtained complex knowledge assertions can be exploited for constructing a user profile and for improving the quality of present Web search tools.KeywordsInformation SourceDescription LogicSemantic DistanceClass ExpressionEuropean Social FundThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The rapid growth of digital libraries, e-governance, and internet based applications has caused an exponential escalation in the volume of ‘Big-data’ particularly due to texts, images, audios and videos that are being both archived and transmitted on a daily basis. In order to make their storage and transfer efficient, different data compression techniques are used in the literature. The ultimate motive behind data compression is to transform a big size data into small size data, which eventually implies less space while archiving, and less time in transferring. However, in order to operate/analyze compressed data, it is usually necessary to decompress it, so as to bring back the data to its original form, which unfortunately warrants an additional computing cost. In this backdrop, if operating upon the compressed data itself can be made possible without going through the stage of decompression, then the advantage that could be accomplished due to compression would escalate. Further due to compression, from the data structure and storage perspectives, the original visibility structure of the data also being lost, it turns into a potential challenge to trace the original information in the compressed representation. This challenge is the motivation behind exploring the idea of direct processing on the compressed data itself in the literature. The proposed survey paper specifically focuses on compressed document images and brings out two original contributions. The first contribution is that it presents a critical study on different image analysis and image compression techniques, and highlights the motivational reasons for pursuing document image analysis in the compressed domain. The second contribution is that it summarizes the different compressed domain techniques in the literature so far based on the type of compression and operations performed by them. Overall, the paper aims to provide a perspective for pursuing further research in the area of document image analysis and pattern recognition directly based on the compressed data.

The analysis of inconsistent formulas finds an ever-increasing range of applications, that include axiom pinpointing in description logics, fault localization in software, model-based diagnosis, optimization problems, but also explainability of machine learning models. This paper overviews approaches for analyzing inconsistent formulas, focusing on finding and enumerating explanations of and corrections for inconsistency, but also on solving optimization problems modeled as inconsistent formulas.

In the scientific domain, extremely large amounts of data are generated by large-scale high performance computing (HPC) simulations. Storing and sending such vast volumes of data poses serious scalability and performance issues, which can be considerably mitigated by data compression techniques which significantly reduced storage size and data movement burdens. Since scientific data are being shared by scientists more and more frequently, data security methods that ensure the confidentiality, integrity, and availability of data itself are becoming increasingly important. As such, combing compression and encryption is critical to storing large-scale datasets securely. In this work, we explore how to integrate data compression and cryptography techniques as efficiently as possible for big scientific datasets in the HPC field. We perform thorough experiments using different scientific datasets with the state-of-the-art error-bounded lossy compressor - SZ - on a real-world supercomputing environment. Experiments verify that performing encryption before lossy compression (a.k.a., encr-cmpr method) may invalidate the advantage of compression algorithms. By contrast, executing encryption after lossy compression (a.k.a., cmpr-encr method) keeps not only high compression ratios but high overall execution speed. Experiments also verify that the encryption overhead under the cmpr-encr method decreases with increasing compression ratios, which means very good scalability.

Decision makers (humans or software agents alike) are faced with the challenge of examining large volumes of information originating from heterogeneous sources with the goal of ascertaining trust in various pieces of information. In this paper we argue (using examples) that traditional trust models are limited in their data model by assuming a pair-wise numeric rating between two entities (e.g., eBay recommendations, Netflix movie rating, etc). We present a novel trust computational model for rich, complex and uncertain information encoded using Bayesian Description Logics. We present security and scalability tradeoffs that arise in the new model, and the results of an evaluation of the first prototype implementation under a variety attack scenarios.