Previous Queries Research Articles

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This article presents the features of the use of the recognition algorithm of the text by method "slitherring widening window" for coding the plural reasons to deaths. The used algorithm dynamically "adjusts" degree of the coincidence and finds the most similar variant, as well as allows to recognize the text with grammatical errors and with ceased word in wording of the reason to deaths. 
 The authors propose three variants to realization of the recognition algorithm of the text, that increase the speed of action. The first variant is based on the elimination of one of its replacement cycle by calculating the simultaneous windows with different sizes (1 to 16). The second variant is based on the pre-filter, for example, by scanning three letters, and the use of the intermediate base for receiving the filtered information. This option allows you to reduce the amount of sorted data and thereby increases performance. The third variant based on the filtering, involves sorting information in the query executed on the basis of previous information query filtration. The advantages and disadvantages identified for each option. The results were evaluated on the speed of action and accuracy of recognition. In this framework, there were 8472 languages used for encoding of multiple causes of death.
 The described analysis is useful in developing a software module used to register mortality. It is recom-mended the third option, based on the filter, to implement the language Visual C ++.

Efficient tag reading protocol for large-scale RFID systems with pre-reading

In large-scale RFID systems, collecting all of the tag IDs is a time-consuming process. A protocol designated as Smart Trend-Traversal (STT) has been proposed to reduce collisions during the tag collection process and to dynamically construct the query strings used to interrogate the tags. In general, if the tag ID information is known to the reader from a previous tag collection round, the efficiency of the current round can be significantly improved. Various protocols have been proposed for scanning a known tag set based on the use of a hash function. Accordingly, the present study proposes an Enhanced STT scheme based on a blocking protocol and a Distributed Record Tag-Check (DRTC) mechanism. Compared to the conventional STT scheme, the proposed protocol adaptively adjusts the length of the query string depending on the response received to the previous query. Moreover, in the DRTC mechanism, the tags determine their transmission slot frame directly without the assistance of the reader, and thus the overall overhead of the tag-collection process is reduced. The simulation results show that the Enhanced STT scheme reduces the total number of queries required to collect the entire tag set compared to the conventional STT method. Moreover, the proposed DRTC mechanism yields an effective reduction in the total number of frame slots compared to existing protocols such as TPP/CSTR and ECRB.

SAFQuery: a simple and flexible advanced Web search interface

Purpose This paper aims to introduce a prototype system called SAFQuery (Simple And Flexible Query interface). In many existing Web search interfaces, simple and advanced query processes are treated separately that cannot be issued interchangeably. In addition, after several rounds of queries for specific information need(s), it is possible that users might wish to re-examine the retrieval results corresponding to some previous queries or to slightly modify some of the specific queries issued before. However, it is often hard to remember what queries have been issued. These factors make the current Web search process not very simple or flexible. Design/methodology/approach In SAFQuery, the simple and advanced query strategies are integrated into a single interface, which can easily formulate query specifications when needed in the same interface. Moreover, query history information is provided that displays the past query specifications, which can help with the memory load. Findings The authors' experiments by user evaluation show that most users had a positive experience when using SAFQuery. Specifically, it is easy to use and can simplify the Web search task. Originality/value The proposed prototype system provides simple and flexible Web search strategies. Particularly, it allows users to easily issue simple and advanced queries based on one single query interface, interchangeably. In addition, users can easily input previously issued queries without spending time to recall what the queries are and/or to re-type previous queries.

Context-aware semantic classification of search queries for browsing community question–answering archives

Community question answering (cQA) platforms, like Yahoo! Answers, provide standard search APIs to browse past questions and answers. Since, the largest cQA services maintain massive sets of resolved questions, the necessity for effective methods to revitalize the information contained in their archives is getting more and more important to serve the needs of their members as promptly and as reliably as possible.In this paper, we present a novel strategy for effectively browsing cQA archives. The core idea is to induce the semantic classes of question-like search queries (e.g., “rib pain after ovulation” and “iron oxide household”) by means of the contextual information set up or represented by inferred views of their respective search sessions, namely views modeling previous queries entered by the same user.When searching cQA archives, members do not associate semantic classes to their queries, so we are considering the cQA as a knowledge base that defines a taxonomy of semantic classes for their posted questions, which provides an explicit mapping between search queries and these questions. Following a supervised learning approach, we investigate and analyze the most salient features that are necessary to automatically exploit this relevant cQA mapping.We carried out a large number of experiments using a rich set of attributes extracted from an automatically acquired big dataset from Yahoo! Answers. Our results confirm what was often only intuitively assumed, namely, that larger contexts actually help to detect semantic relations implicitly expressed in sequences of queries submitted by users during their search sessions. In particular, we discover that Explicit Semantic Analysis is extremely helpful for inferring discriminative semantic cues that reduce, and thus determine, the semantic range of question-like search queries. Conversely, constructing traditional bag-of-words models on top of prior queries in the session was detrimental.

Reconstructing edge-disjoint paths faster

For a simple undirected graph with n vertices and m edges, we consider a data structure that given a query of a pair of vertices u, v and an integer k≥1, it returns k edge-disjoint uv-paths. The data structure takes Õ(n3.375) time to build, using O(mn1.5logn) space, and each query takes O(kn) time, which is optimal and beats the previous query time of O(knα(n)).

Learning semantic representation with neural networks for community question answering retrieval

In community question answering (cQA), users pose queries (or questions) on portals like Yahoo! Answers which can then be answered by other users who are often knowledgeable on the subject. cQA is increasingly popular on the Web, due to its convenience and effectiveness in connecting users with queries and those with answers. In this article, we study the problem of finding previous queries (e.g., posed by other users) which may be similar to new queries, and adapting their answers as the answers to the new queries. A key challenge here is to the bridge the lexical gap between new queries and old answers. For example, “company” in the queries may correspond to “firm” in the answers. To address this challenge, past research has proposed techniques similar to machine translation that “translate” old answers to ones using the words in the new queries. However, a key limitation of these works is that they assume queries and answers are parallel texts, which is hardly true in reality. As a result, the translated or rephrased answers may not look intuitive.In this article, we propose a novel approach to learn the semantic representation of queries and answers by using a neural network architecture. The learned semantic level features are finally incorporated into a learning to rank framework. We have evaluated our approach using a large-scale data set. Results show that the approach can significantly outperform existing approaches.

جدلية البناء تحت الأرض Dialectic of Underground Building Underground Building between Feature Acceptance and Constraints rejection

تظهر مشکلة البحث فى جدلية الخلاف حول البناء تحت الأرض ما بين مؤيد للفکرة لمميزاته وبين معارض لهذا البناء لعوائقه وکل منهما له دلائله، يحاول البحث الوقوف على مميزات وعوائق البناء تحت الأرض إسهاما فى إيجاد الحلول الملائمة لهذه العيوب والعوائق، الوقوف على مدى ملائمة هذا البناء لأي من الأنشطة سواء سکنية أو تجارية أو ثقافية، فى مصر، وأى الأماکن والمواقع المناسبة فى حالة إمکانية البناء تحت الأرض وذلک فى ظل مناخ حار جاف يزداد سوء مع تزايد الصناعة وإهمال الطبيعة. مع مناقشة إمکانية مدى مساهمة تطبيق مبادئ الاستدامة والعمارة الخضراء وتوفير الطاقة فى هذا النوع من البناء لتحقيق أکبر استفادة ممکنة. يحاول البحث الإجابة عن التساؤلات السابقة، لذا ينتهج البحث المنهج التحليلى لبعض نماذج البناء تحت الأرض سواء الناجحة فى تأدية وظيفته أو العکس طبقا لمبادئ الاستدامة. The problem of researching the dialectic of underground building approach appears clearly amongst the supporters for the approach advantages and the oppositionists with its disadvantages where each party is backed up with its own evidences. This research is investigating both features and obstacles of underground building in contribution to finding the appropriate solutions for the obstacles and defects while confirming the underground building fitting with various activities (i.e. residential, commercial or cultural) in Egypt. In addition to investigate which are the suitable places or locations in the case of building underground in such a hot and dry weather with the industry expansion and nature negligence. It will discuss the possibility of applying the sustainability principle, green architecture and energy saving in the underground building in order to achieve the highest benefits. This research attempts to investigate the previous queries, and so it follows an analytical approach of some underground buildings Examples, whether they are functioning successfully or not according to the sustainability principle.

R3D-2-MSA: the RNA 3D structure-to-multiple sequence alignment server.

The RNA 3D Structure-to-Multiple Sequence Alignment Server (R3D-2-MSA) is a new web service that seamlessly links RNA three-dimensional (3D) structures to high-quality RNA multiple sequence alignments (MSAs) from diverse biological sources. In this first release, R3D-2-MSA provides manual and programmatic access to curated, representative ribosomal RNA sequence alignments from bacterial, archaeal, eukaryal and organellar ribosomes, using nucleotide numbers from representative atomic-resolution 3D structures. A web-based front end is available for manual entry and an Application Program Interface for programmatic access. Users can specify up to five ranges of nucleotides and 50 nucleotide positions per range. The R3D-2-MSA server maps these ranges to the appropriate columns of the corresponding MSA and returns the contents of the columns, either for display in a web browser or in JSON format for subsequent programmatic use. The browser output page provides a 3D interactive display of the query, a full list of sequence variants with taxonomic information and a statistical summary of distinct sequence variants found. The output can be filtered and sorted in the browser. Previous user queries can be viewed at any time by resubmitting the output URL, which encodes the search and re-generates the results. The service is freely available with no login requirement at http://rna.bgsu.edu/r3d-2-msa.

A continuous reverse skyline query processing method in moving objects environments

Many studies on reverse skyline query processing have been done for various services. The existing reverse skyline query processing methods are based on dynamic skylines. There are no reverse skyline query processing algorithms based on metric spaces for location-based services. The existing methods for processing a reverse skyline query have the limitation of service domains and require the high costs of computation to provide various location-based services. In this paper, we propose a new reverse skyline query processing method that efficiently processes a query over the moving objects. In addition, the proposed method processes a continuous reverse skyline query efficiently. In order to show the superiority of the proposed method, we compare it with the previous reverse skyline query processing method in various environments. As a result, the proposed method achieves better performance than the existing method.

Is 'self-medication' a useful term to retrieve related publications in the literature? A systematic exploration of related terms.

BackgroundSelf-Medication (SM), i.e. using medications to treat oneself, is a major concern for health researchers and policy makers. The terms “self medication” or “self-medication” (SM terms) have been used to explain various concepts while several terms have also been employed to define this practice. Hence, retrieving relevant publications would require exhaustive literature screening. So, we assessed the current situation of SM terms in the literature to improve the relevancy of search outcomes.MethodsIn this Systematic exploration, SM terms were searched in the 6 following databases and publisher’s portals till April 2012: Web of Science, Scopus, PubMed, Google scholar, ScienceDirect, and Wiley. A simple search query was used to include only publications with SM terms. We used Relative-Risk (RR) to estimate the probability of SM terms use in related compared to unrelated publications. Sensitivity and specificity of SM terms as keywords in search query were also calculated. Relevant terms to SM practice were extracted and their Likelihood Ratio positive and negative (LR+/-) were calculated to assess their effect on the probability of search outcomes relevancy in addition to previous search queries. We also evaluated the content of unrelated publications. All mentioned steps were performed in title (TI) and title or abstract (TIAB) of publications.Results1999 related and 1917 unrelated publications were found. SM terms RR was 4.5 in TI and 2.1 in TIAB. SM terms sensitivity and specificity respectively were 55.4% and 87.7% in TI and 84.0% and 59.5% in TIAB. “OTC” and “Over-The-Counter Medication”, with LR+ 16.78 and 16.30 respectively, provided the most conclusive increase in the probability of the relevancy of publications. The most common unrelated SM themes were self-medication hypothesis, drug abuse and Zoopharmacognosy.ConclusionsDue to relatively low specificity or sensitivity of SM terms, relevant terms should be employed in search queries and clear definitions of SM applications should be applied to improve the relevancy of publications.

Identifying codes and searching with balls in graphs

Given a graph G and a positive integer R we address the following combinatorial search theoretic problem: What is the minimum number of queries of the form “does an unknown vertex v∈V(G) belong to the ball of radius r around u?” with u∈V(G) and r≤R that is needed to determine v. We consider both the adaptive case when the jth query might depend on the answers to the previous queries and the non-adaptive case when all queries must be made at once. We obtain bounds on the minimum number of queries for hypercubes, the Erdős–Rényi random graphs and graphs of bounded maximum degree.

Evaluation of PraedicoTM, A Next Generation Big Data Biosurveillance Application

We conducted an initial assessment of the biosurveillance capabilities of a new software application called Praedico and compared results obtained from previous queries with the ESSENCE.

IJARCCE - Computer and Communication Engineering

The way of getting different and explosive growth of Web information effectively is more critical. The first challenge is that it is not easy to recommend latent semantically relevant results to users. In the Query Suggestion, there are several outstanding issues that can potentially degrade the quality of the recommendations. The first one is the ambiguity which commonly exists in the natural language. Queries containing ambiguous terms may confuse the algorithms which do not satisfy the information needs of users. This research cope with the abbreviation based Query suggestion to provide the recommendation for user query, based on the short term query word. It also proposes K- means clustering Algorithm for generating user group based query matrix. In previous search query words are also taken into query suggestion calculation. In this previous query based suggestion method not consider the Zero Visit count URLs. But in normal find query suggestion also consider the Zero visit count URLs. The Graph query suggestion construct the Bipartite directed graph, based on the given queries and the access URLs. In addition Find Query Suggestion is constructed for domain wise. In addition, to add the recommendation user details play an important role in online query suggestion calculation. The view recommendation user URL access details to provide the user URL access details, it is used to the user can easily identify which one is the best URL for the given query. At run time user URL access matrix to provide all users URL access details and user group based query matrix which is used to give the user access details by group wise. In addition the image can be searched based on the suggested query words. In this proposed method the images can be added under the category wise and image search results returns all the images which is added under the category.

Ranking and Optimizing of Location Based Services by User’s Behavioral Patterns Using Data Mining Techniques

The utilization of the mobile technology devices is growing more and more, and the world is shrinking into a mobile. Recent advances in mobile technologies have meant that the technical capability to record and transmit location data for processing is appearing in off-the-shelf handsets. This opens possibilities to get the profile of the people based on the places they visit, people they associate with, or other aspects of their complex routines determined through persistent tracking using GPS enabled mobile phones. There must be recording of the users behaviors about the location information and stored it into the database. The services can be provided to offer customized information based on the results of such behavioral profiling could become commonplace. The GPS locations of the people have been mined to reveal places of interest and to create simple profiles. The information drawn from the profiling activity ranges from intuitive through special cases to insightful. User can send their desired query to find out the particular shop or object in real-time. This provides an efficient response to the user regarding their query, and it distinguishes which place is famous for which thing based on the previous queries from several users.

Study on Segmentation of User’s Query Intents

Query intent denotes the specific information that user wants to look for when he submit a query to a search engine.If search engine can judge whether the intent of current query is same as that of previous query,they would refine current query and offer users more accurate results and services,such as query suggestion,query expansion or personalized information retrieval.This paper proposed click data-based similarity,and this similarity is effective with decision tree model and CRF model.Users' click data is helpful for this task,with users' click data,F-measure is improved by 1% and 1.4% for decision tree-based method and CRF-based method respectively.

A vlHMM approach to context-aware search

Capturing the context of a user's query from the previous queries and clicks in the same session leads to a better understanding of the user's information need. A context-aware approach to document reranking, URL recommendation, and query suggestion may substantially improve users' search experience. In this article, we propose a general approach to context-aware search by learning a variable length hidden Markov model ( vlHMM ) from search sessions extracted from log data. While the mathematical model is powerful, the huge amounts of log data present great challenges. We develop several distributed learning techniques to learn a very large vlHMM under the map-reduce framework. Moreover, we construct feature vectors for each state of the vlHMM model to handle users' novel queries not covered by the training data. We test our approach on a raw dataset consisting of 1.9 billion queries, 2.9 billion clicks, and 1.2 billion search sessions before filtering, and evaluate the effectiveness of the vlHMM learned from the real data on three search applications: document reranking, query suggestion, and URL recommendation. The experiment results validate the effectiveness of vlHMM in the applications of document reranking, URL recommendation, and query suggestion.

Query Expansion Based on Books Tags of User’s Social Relations in XML Book Search

Queries provided by users are usually too fuzzy and simple, which cannot provide sufficient evidence for the book search system to find relevant books. Previous query expansion focused on expanding similar words for query, which may not represent different user real-time interested. Since the user friends and interesting libraries may represent the interesting book types for the user for a certain stage, this paper proposed a novel method by finding expanded words from the books tags marked by user social relations in their personal digital library. This paper presents two query expansion methods: Query expansion based on books tags of user friends; Query expansion based on books tags of user interesting libraries. Experiments on the actual book dataset show that the two methods greatly improve the nDCG@10 values of the search results and the user satisfaction.

A Pattern-Matching Scheme With High Throughput Performance and Low Memory Requirement

Pattern-matching techniques have recently been applied to network security applications such as intrusion detection, virus protection, and spam filters. The widely used Aho-Corasick (AC) algorithm can simultaneously match multiple patterns while providing a worst-case performance guarantee. However, as transmission technologies improve, the AC algorithm cannot keep up with transmission speeds in high-speed networks. Moreover, it may require a huge amount of space to store a two-dimensional state transition table when the total length of patterns is large. In this paper, we present a pattern-matching architecture consisting of a stateful pre-filter and an AC-based verification engine. The stateful pre-filter is optimal in the sense that it is equivalent to utilizing all previous query results. In addition, the filter can be easily realized with bitmaps and simple bitwise-AND and shift operations. The size of the two-dimensional state transition table in our proposed architecture is proportional to the number of patterns, as opposed to the total length of patterns in previous designs. Our proposed architecture achieves a significant improvement in both throughput performance and memory usage.

K-Order Surrounding Roadmaps Path Planner for Robot Path Planning

Probabilistic roadmaps are commonly used in robot path planning. Most sampling-based path planners often produce poor-quality roadmaps as they focus on improving the speed of constructing roadmaps without paying much attention to the quality. Poor-quality roadmaps can cause problems such as poor-quality paths, time-consuming path searching and failures in the searching. This paper presents a K-order surrounding roadmap (KSR) path planner which constructs a roadmap in an incremental manner. The planner creates a tree while answering a query, selects the part of the tree according to quality measures and adds the part to an existing roadmap which is obtained in the same way when answering the previous queries. The KSR path planner is able to construct high-quality roadmaps in terms of good coverage, high connectivity, provision of alternative paths and small size. Comparison between the KSR path planner and Reconfigurable Random Forest (RRF), an existing incremental path planner, as well as traditional probabilistic roadmap (PRM) path planner shows that the roadmaps constructed using the KSR path planner have higher quality that those that are built by the other planners.

RDFBuilder: A tool to automatically build RDF-based interfaces for MAGE-OM microarray data sources

This paper presents RDFBuilder, a tool that enables RDF-based access to MAGE-ML-compliant microarray databases. We have developed a system that automatically transforms the MAGE-OM model and microarray data stored in the ArrayExpress database into RDF format. Additionally, the system automatically enables a SPARQL endpoint. This allows users to execute SPARQL queries for retrieving microarray data, either from specific experiments or from more than one experiment at a time. Our system optimizes response times by caching and reusing information from previous queries. In this paper, we describe our methods for achieving this transformation. We show that our approach is complementary to other existing initiatives, such as Bio2RDF, for accessing and retrieving data from the ArrayExpress database.