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Current social-network-based and location-based-service applications need to handle continuous spatial approximate keyword queries over geo-textual streaming data of high density. The continuous query is a well-known expensive operation. The optimization of continuous query processing is still an open issue. For geo-textual streaming data, the performance issue is more serious since both location information and textual description need to be matched for each incoming streaming data tuple. The state-of-the-art continuous spatial-keyword query indexing approaches generally lack both support for approximate keyword matching and high-performance processing for geo-textual streaming data. Aiming to tackle this problem, this paper first proposes an indexing approach for efficient supporting of continuous spatial approximate keyword queries by integrating m i n - w i s e signatures into an AP-tree, namely AP-tree + . AP-tree + utilizes the one-permutation m i n - w i s e hashing method to achieve a much lower signature maintenance costs compared with the traditional m i n - w i s e hashing method because it only employs one hashing function instead of dozens. Towards providing a more efficient indexing approach, this paper has explored the feasibility of parallelizing AP-tree + by employing a Graphic Processing Unit (GPU). We mapped the AP-tree + data structure into the GPU&rsquo, s memory with a variety of one-dimensional arrays to form the GPU-aided AP-tree + . Furthermore, a m i n - w i s e parallel hashing algorithm with a scheme of data parallel and a GPU-CPU data communication method based on a four-stage pipeline way have been used to optimize the performance of the GPU-aided AP-tree + . The experimental results indicate that (1) AP-tree + can reduce the space cost by about 11% compared with MHR-tree, (2) AP-tree + can hold a comparable recall and 5.64&times, query performance gain compared with MHR-tree while saving 41.66% maintenance cost on average, (3) the GPU-aided AP-tree + can attain an average speedup of 5.76&times, compared to AP-tree + , and (4) the GPU-CPU data communication scheme can further improve the query performance of the GPU-aided AP-tree + by 39.4%.

International audience; Social media provides an environment of information exchange. They principally rely on their users to create content, to annotate others’ content and to make on-line relationships. The user activities reflect his opinions, interests, etc. in this environment. We focus on analysing this social environment to detect user interests which are the key elements for improving adaptation. This choice is motivated by the lack of information in the user profile and the inefficiency of the information issued from methods that analyse the classic user behaviour (e.g. navigation, time spent on web page, etc.). So, having to cope with an incomplete user profile, the user social network can be an important data source to detect user interests. The originality of our approach is based on the proposal of a new technique of interests' detection by analysing the accuracy of the tagging behaviour of a user in order to figure out the tags which really reflect the content of the resources. So, these tags are somehow comprehensible and can avoid tags “ambiguity” usually associated to these social annotations. The approach combines the tag, user and resource in a way that guarantees a relevant interests detection. The proposed approach has been tested and evaluated in the Delicious social database. For the evaluation, we compare the result issued from our approach using the tagging behaviour of the neighbours (the egocentric network and the communities) with the information yet known for the user (his profile). A comparative evaluation with the classical tag-based method of interests detection shows that the proposed approach is better.

Background Facing a growing workload and dwindling resources, the US National Library of Medicine (NLM) created the Indexing Initiative project in 1996. This cross-library team’s mission is to explore indexing methodologies for ensuring quality and currency of NLM document collections. The NLM Medical Text Indexer (MTI) is the main product of this project and has been providing automated indexing recommendations since 2002. After all of this time, the questions arise whether MTI is still useful and relevant. Methods To answer the question about MTI usefulness, we track a wide variety of statistics related to how frequently MEDLINE indexers refer to MTI recommendations, how well MTI performs against human indexing, and how often MTI is used. To answer the question of MTI relevancy compared to other available tools, we have participated in the 2013 and 2014 BioASQ Challenges. The BioASQ Challenges have provided us with an unbiased comparison between the MTI system and other systems performing the same task. Results Indexers have continually increased their use of MTI recommendations over the years from 15.75% of the articles they index in 2002 to 62.44% in 2014 showing that the indexers find MTI to be increasingly useful. The MTI performance statistics show significant improvement in Precision (+0.2992) and F1 (+0.1997) with modest gains in Recall (+0.0454) over the years. MTI consistency is comparable to the available indexer consistency studies. MTI performed well in both of the BioASQ Challenges ranking within the top tier teams. Conclusions Based on our findings, yes, MTI is still relevant and useful, and needs to be improved and expanded. The BioASQ Challenge results have shown that we need to incorporate more machine learning into MTI while still retaining the indexing rules that have earned MTI the indexers’ trust over the years. We also need to expand MTI through the use of full text, when and where it is available, to provide coverage of indexing terms that are typically only found in the full text. The role of MTI at NLM is also expanding into new areas, further reinforcing the idea that MTI is increasingly useful and relevant.

We consider the recoverability of traditional R-tree index structures under concurrent updating transactions, an important issue that is neglected or treated inadequately in many proposals of R-tree concurrency control. We present two solutions to ARIES-based recovery of transactions on R-trees. These assume a standard fine-grained single-version update model with physiological write-ahead logging and steal-and-no-force buffering where records with uncommitted updates by a transaction may migrate from their original page to another page due to structure modifications caused by other transactions. Both solutions guarantee that an R-tree will remain in a consistent and balanced state in the presence of any number of concurrent forward-rolling and (totally or partially) backward-rolling multiaction transactions and in the event of process failures and system crashes. One solution maintains the R-tree in a strictly consistent state in which the bounding rectangles of pages are as tight as possible, while in the other solution this requirement is relaxed. In both solutions only a small constant number of simultaneous exclusive latches (write latches) are needed, and in the solution that only maintains relaxed consistency also the number of simultaneous nonexclusive latches is similarly limited. In both solutions, deletions are handled uniformly with insertions, and a logarithmic insertion-path length is maintained under all circumstances.

We present a query formulation language (called MashQL) in order to easily query and fuse structured data on the web. The main novelty of MashQL is that it allows people with limited IT skills to explore and query one (or multiple) data sources without prior knowledge about the schema, structure, vocabulary, or any technical details of these sources. More importantly, to be robust and cover most cases in practice, we do not assume that a data source should havean offline or inlineschema. This poses several language-design and performance complexities that we fundamentally tackle. To illustrate the query formulation power of MashQL, and without loss of generality, we chose the Data web scenario. We also chose querying RDF, as it is the most primitive data model hence, MashQL can be similarly used for querying relational databases and XML. We present two implementations of MashQL, an online mashup editor, and a Firefox add on. The former illustrates how MashQL can be used to query and mash up the Data web as simple as filtering and piping web feeds and the Firefox add on illustrates using the browser as a web composer rather than only a navigator. To end, we evaluate MashQL on querying two data sets, DBLP and DBPedia, and show that our indexing techniques allow instant user interaction. © 1989-2012 IEEE. 24 5 783 798 Cited By :15

Retrieving text from early printed books is particularly difficult because in these documents, the words are very close one to the other and, similarly to medieval manuscripts, there is a large use of ligatures and abbreviations. To address these problems, we propose a word indexing and retrieval technique that does not require word segmentation and is tolerant to errors in character segmentation. Two main principles characterize the approach. First, characters are identified in the pages and clustered with self-organizing map (SOM). During the retrieval, the similarity of characters is estimated considering the proximity of cluster centroids in the SOM space, rather than directly comparing the character images. Second, query words are matched with the indexed sequence of characters by means of a dynamic time warping (DTW)-based approach. The proposed technique integrates the SOM similarity and the information about the width of characters in the string matching process. The best path in the DTW array is identified considering the widths of matching words with respect to the query so as to deal with broken or touching symbols. The proposed method is tested on four copies of the Gutenberg Bibles.

Special methods are required to interpret sparse diffraction patterns collected from peptide crystals at X-ray free-electron lasers. Bragg spots can be indexed from composite-image powder rings, with crystal orientations then deduced from a very limited number of spot positions., Still diffraction patterns from peptide nanocrystals with small unit cells are challenging to index using conventional methods owing to the limited number of spots and the lack of crystal orientation information for individual images. New indexing algorithms have been developed as part of the Computational Crystallography Toolbox (cctbx) to overcome these challenges. Accurate unit-cell information derived from an aggregate data set from thousands of diffraction patterns can be used to determine a crystal orientation matrix for individual images with as few as five reflections. These algorithms are potentially applicable not only to amyloid peptides but also to any set of diffraction patterns with sparse properties, such as low-resolution virus structures or high-throughput screening of still images captured by raster-scanning at synchrotron sources. As a proof of concept for this technique, successful integration of X-ray free-electron laser (XFEL) data to 2.5 Å resolution for the amyloid segment GNNQQNY from the Sup35 yeast prion is presented.

We describe one tool for Table of Content (ToC) identification and recognition from PDF books. This task is part of ongoing research on the development of tools for the semi-automatic conversion of PDF documents in the Epub format that can be read on several E-book devices. Among various sub-tasks, the ToC extraction and recognition is particularly useful for an easy navigation of book contents.The proposed tool first identifies the ToC pages. The bounding boxes of ToC titles in the book body are subsequently found in order to add suitable links in the Epub ToC. The proposed approach is tolerant to discrepancies between the ToC text and the corresponding titles. We evaluated the tool on several open access books edited by University Presses that are partner of the OAPEN EcontentPlus project

MashQL, a novel query formulation language for querying and mashing up structured data on the Web, doesn't require users to know the queried data's structure or the data itself to adhere to a schema. In this article, the authors address the fact that being a language, not merely an interface (and simultaneously schema-free), MashQL faces a particular set of challenges. In particular, the authors propose and evaluate a novel technique for optimizing queries over large data sets to allow instant user interaction. © 2006 IEEE. 14 3 58 67 Cited By :9