Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

A web services based framework for efficient monitoring and event reporting.

Network and Service Management (NSM) is a research discipline with significant research contributions the last 25 years. Despite the numerous standardised solutions that have been proposed for NSM, the quest for an "all encompassing technology" still continues. A new technology introduced lately to address NSM problems is Web Services (WS). Despite the research effort put into WS and their potential for addressing NSM objectives, there are efficiency, interoperability, etc issues that need to be solved before using WS for NSM. This thesis looks at two techniques to increase the efficiency of WS management applications so that the latter can be used for efficient monitoring and event reporting. The first is a query tool we built that can be used for efficient retrieval of management state data close to the devices where they are hosted. The second technique is policies used to delegate a number of tasks from a manager to an agent to make WS-based event reporting systems more efficient. We tested the performance of these mechanisms by incorporating them in a custom monitoring and event reporting framework and supporting systems we have built, against other similar mechanisms (XPath) that have been proposed for the same tasks, as well as previous technologies such as SNMP. Through these tests we have shown that these mechanisms are capable of allowing us to use WS efficiently in various monitoring and event reporting scenarios. Having shown the potential of our techniques we also present the design and implementation challenges for building a GUI tool to support and enhance the above systems with extra capabilities. In summary, we expect that other problems WS face will be solved in the near future, making WS a capable platform for it to be used for NSM

Interoperability of XML and relational data-optimization algorithm

"Within the past six years, Extensible Markup Language (XML) has spread rapidly and has gained popularity in the database community with its primary focus in the design of query languages and storage methods to select data from vast amounts of XML data efficiently. In this respect, I discuss some of the research that has been done by presenting three papers that describe different approaches to querying XML documents. This thesis concentrates on the method used by Sadri and Lakshmanan in [1]: viewing an XML document as a relational database upon which the user can write simple SQL queries that can be translated into equivalent XQuery queries. Taking the output of the translation algorithm presented, I further develop an optimization algorithm meant to decrease the running time of the translated queries. I mainly focus on two aspects: the need of the distinct-values() function and the minimization of the number of variables. "--Abstract from author supplied metadata

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Efficiency and effectiveness of XML keyword search using a full element index

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2010.Thesis (Master's) -- Bilkent University, 2010.Includes bibliographical references leaves 63-67.In the last decade, both the academia and industry proposed several techniques to allow keyword search on XML databases and document collections. A common data structure employed in most of these approaches is an inverted index, which is the state-of-the-art for conducting keyword search over large volumes of textual data, such as world wide web. In particular, a full element-index considers (and indexes) each XML element as a separate document, which is formed of the text directly contained in it and the textual content of all of its descendants. A major criticism for a full element-index is the high degree of redundancy in the index (due to the nested structure of XML documents), which diminishes its usage for large-scale XML retrieval scenarios. As the rst contribution of this thesis, we investigate the e ciency and e ectiveness of using a full element-index for XML keyword search. First, we suggest that lossless index compression methods can signi cantly reduce the size of a full element-index so that query processing strategies, such as those employed in a typical search engine, can e ciently operate on it. We show that once the most essential problem of a full element-index, i.e., its size, is remedied, using such an index can improve both the result quality (e ectiveness) and query execution performance (e ciency) in comparison to other recently proposed techniques in the literature. Moreover, using a full element-index also allows generating query results in di erent forms, such as a ranked list of documents (as expected by a search engine user) or a complete list of elements that include all of the query terms (as expected by a DBMS user), in a uni ed framework. As a second contribution of this thesis, we propose to use a lossy approach, static index pruning, to further reduce the size of a full element-index. In this way, we aim to eliminate the repetition of an element's terms at upper levels in an adaptive manner considering the element's textual content and search system's ranking function. That is, we attempt to remove the repetitions in the index only when we expect that removal of them would not reduce the result quality. We conduct a well-crafted set of experiments and show that pruned index les are comparable or even superior to the full element-index up to very high pruning levels for various ad hoc tasks in terms of retrieval e ectiveness. As a nal contribution of this thesis, we propose to apply index pruning strategies to reduce the size of the document vectors in an XML collection to improve the clustering performance of the collection. Our experiments show that for certain cases, it is possible to prune up to 70% of the collection (or, more speci cally, underlying document vectors) and still generate a clustering structure that yields the same quality with that of the original collection, in terms of a set of evaluation metrics.Atılgan, DuyguM.S

Usability and expressiveness in database keyword search : bridging the gap

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