1,297 research outputs found

    Reasoning & Querying – State of the Art

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    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

    Squential Step Towards Pattern Warehousing

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    With the massive increase in the data, the demand by the analysts hyped for the proper repositories where they could analyse the concerned specific data patterns in order to make smart and quick decisions for the welfare and benefit of the business, organization or some social work. Pattern warehouse proved to be the best solution. This paper focuses on the discussion of existing architecture and moreover on the algorithms that is needed for retrieving the optimal patterns from the pattern warehouse. It also includes the detailed study about the sequential emergence of association rule algorithms which initially derive out patterns and later on those patterns are being optimized according to the interest of the analyst

    Business process model repositories : efficient process retrieval

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    As organizations increasingly work in process-oriented manner, the number of business process models that they develop and have to maintain increases. As a consequence, it has become common for organizations to have collections of hundreds or even thousands of business process models. When a collection contains such a large number of business process models, it is impossible to manage that collection manually. Therefore, Business Process (BP) Model Repositories are required that store large collections of process models and provide techniques for managing these collections automatically and efficiently. The goal of research described in this thesis is to improve on existing BP Model Repositories, by improving the management techniques that are supported by these repositories on an aspect that has received little attention so far. Looking ahead at the results of the research, the aspect that will be selected for improvement is the process retrieval aspect. The two main research activities that will be carried in the context of this research are the following. Firstly, a survey of Business Process Model Repositories is performed to identity an unsolved aspect to be enhanced. The functionality of existing BP Model Repositories is listed and summarized as a framework for BP Model Repositories. After comparing the functionality that is provided by existing BP Model Repositories, based on the framework, efficient process retrieval is selected as the aspect that will be improved. This aspect is selected, because, although existing BP Model Repositories provide techniques for process retrieval, none of them focus on the efficiency of process retrieval. Secondly, an indexing technique for process retrieval (both process similarity search and process querying) is proposed. The index is constructed using features of process models. Features are small and characteristic fragments of process models. As such, by matching features of a given query/search model and features of a process model in a collection, a small set of models in the collection that potentially match the query/search model can be retrieved efficiently through the index. Techniques are also proposed to further check whether a potential match is an actual match for the query/search model. All of the above techniques are implemented as a component of the AProMoRe (an Advanced Process Model Repository) process repository. To evaluate the proposed process retrieval techniques, experiments are run using both real-life and synthetic process model collections. Experimental results show that on average the process retrieval techniques proposed in this thesis performs at least one order of magnitude faster than existing techniques

    Profiling relational data: a survey

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    Profiling data to determine metadata about a given dataset is an important and frequent activity of any IT professional and researcher and is necessary for various use-cases. It encompasses a vast array of methods to examine datasets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, namely correlations, unique column combinations, functional dependencies, and inclusion dependencies. Further techniques detect conditional properties of the dataset at hand. This survey provides a classification of data profiling tasks and comprehensively reviews the state of the art for each class. In addition, we review data profiling tools and systems from research and industry. We conclude with an outlook on the future of data profiling beyond traditional profiling tasks and beyond relational databases

    Semantics and result disambiguation for keyword search on tree data

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    Keyword search is a popular technique for searching tree-structured data (e.g., XML, JSON) on the web because it frees the user from learning a complex query language and the structure of the data sources. However, the convenience of keyword search comes with drawbacks. The imprecision of the keyword queries usually results in a very large number of results of which only very few are relevant to the query. Multiple previous approaches have tried to address this problem. Some of them exploit structural and semantic properties of the tree data in order to filter out irrelevant results while others use a scoring function to rank the candidate results. These are not easy tasks though and in both cases, relevant results might be missed and the users might spend a significant amount of time searching for their intended result in a plethora of candidates. Another drawback of keyword search on tree data, also due to the incapacity of keyword queries to precisely express the user intent, is that the query answer may contain different types of meaningful results even though the user is interested in only some of them. Both problems of keyword search on tree data are addressed in this dissertation. First, an original approach for answering keyword queries is proposed. This approach extracts structural patterns of the query matches and reasons with them in order to return meaningful results ranked with respect to their relevance to the query. The proposed semantics performs comparisons between patterns of results by using different types of ho-momorphisms between the patterns. These comparisons are used to organize the patterns into a graph of patterns which is leveraged to determine ranking and filtering semantics. The experimental results show that the approach produces query results of higher quality compared to the previous ones. To address the second problem, an original approach for clustering the keyword search results on tree data is introduced. The clustered output allows the user to focus on a subset of the results, and to save time and effort while looking for the relevant results. The approach performs clustering at different levels of granularity to group similar results together effectively. The similarity of the results and result clusters is decided using relations on structural patterns of the results defined based on homomor-phisms between path patterns. An originality of the clustering approach is that the clusters are ranked at different levels of granularity to quickly guide the user to the relevant result patterns. An efficient stack-based algorithm is presented for generating result patterns and constructing the clustering hierarchy. The extensive experimentation with multiple real datasets show that the algorithm is fast and scalable. It also shows that the clustering methodology allows the users to effectively retrieve their intended results, and outperforms a recent state-of-the-art clustering approach. In order to tackle the second problem from a different aspect, diversifying the results of keyword search is addressed. Diversification aims to provide the users with a ranked list of results which balances the relevance and redundancy of the results. Measures for quantifying the relevance and dissimilarity of result patterns are presented and a heuristic for generating a diverse set of results using these metrics is introduced

    CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

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    Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research
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