Kinds Of Queries Research Articles

A single quadtree-based algorithm for top-[formula omitted] spatial keyword query

Recent years have witnessed the generation of a massive amount of spatial–textual data. In view of this, a new type of query coined spatial keyword query has been proposed to deal with the location-based services with additional keyword constraint. This paper studies one of the most popular spatial keyword queries called Top-k Spatial Keyword Query(TkSKQ). Explicitly speaking, given a set of objects, a TkSKQ finds the k objects that are closest to the querier with each of these k objects satisfying all the keywords specified by the query. This kind of query is of paramount importance in a variety of application domains such as location-based recommendation and advertisement.The state-of-art algorithm for processing a TkSKQ is highly sensitive to the number of query keywords specified in the query such that its performance degrades significantly with an increase in the number of keywords. To remedy this drawback, this paper proposes a novel mechanism that utilizes an additional keyword list to enhance the efficiency of the existing solution. Based on this indexing technique, our algorithm needs only traverse a single quadtree when processing a TkSKQ. Moreover, we study how to prioritize the keywords in the vocabulary so as to optimize the performance of our technique. Furthermore, we deal with a generalized version of the TkSKQ problem, called HkSKQ. A similar technique can also be useful for solving HkSKQ. Experimental results on both synthetic and real data reveal the superiority of our proposed scheme.

Synthesizing memory models from framework sketches and Litmus tests

A memory consistency model specifies which writes to shared memory a given read may see. Ambiguities or errors in these specifications can lead to bugs in both compilers and applications. Yet architectures usually define their memory models with prose and litmus tests—small concurrent programs that demonstrate allowed and forbidden outcomes. Recent work has formalized the memory models of common architectures through substantial manual effort, but as new architectures emerge, there is a growing need for tools to aid these efforts. This paper presents MemSynth, a synthesis-aided system for reasoning about axiomatic specifications of memory models. MemSynth takes as input a set of litmus tests and a framework sketch that defines a class of memory models. The sketch comprises a set of axioms with missing expressions (or holes). Given these inputs, MemSynth synthesizes a completion of the axioms—i.e., a memory model—that gives the desired outcome on all tests. The MemSynth engine employs a novel embedding of bounded relational logic in a solver-aided programming language, which enables it to tackle complex synthesis queries intractable to existing relational solvers. This design also enables it to solve new kinds of queries, such as checking if a set of litmus tests unambiguously defines a memory model within a framework sketch. We show that MemSynth can synthesize specifications for x86 in under two seconds, and for PowerPC in 12 seconds from 768 litmus tests. Our ambiguity check identifies missing tests from both the Intel x86 documentation and the validation suite of a previous PowerPC formalization. We also used MemSynth to reproduce, debug, and automatically repair a paper on comparing memory models in just two days.

Efficient Spatial Keyword Query Processing in the Internet of Industrial Vehicles

With the development of the Internet of Things (IoT), the industrial vehicle ad hoc networks are revolving into the Internet of Industrial Vehicles (IoIV). Due to the popularity of the geographical devices used on the Industrial vehicle, location-based information is extensively available in IoIV. This development calls for spatial keyword queries (SKQ), which takes into account both the locations and textual descriptions of objects. This paper addresses the issue of processing SKQ in IoIV environment, which focuses on two types of SKQ queries, namely Boolean kNN Queries and Top-k Queries. A general air index called Extended Spatial Keyword query index in IoIV environment (ESKIV) is proposed, which supports both network space pruning and textual pruning simultaneously. Based on ESKIV, efficient algorithms are designed to deal with these two types of SKQ respectively. The proposed ESKIV also can be used to deal with other kinds of queries, such as range SKQ. Finally, extensive simulations are conducted to demonstrate the efficiency of our ESKIV index and the corresponding query processing algorithms.

Framework for Managing Uncertain Distributed Categorical Data

In recent years, data has become uncertain due to the flourishing advanced technologies that participate continuously and increasingly in producing large amounts of incomplete data. Often, many modern applications where uncertainty occurs are distributed in nature, e.g., distributed sensor networks, information extraction, data integration, social network, etc. Consequently, even though the data uncertainty has been studied in the past for centralized behavior, it is still a challenging issue to manage uncertainty over the data in situ. In this paper, we propose a framework to managing uncertain categorical data over distributed environments that is built upon a hierarchical indexing technique based on inverted index, and a distributed algorithm to efficiently process queries on uncertain data in distributed environment. Leveraging this indexing technique, we address two kinds of queries on the distributed uncertain databases 1) a distributed probabilistic thresholds query, where its answers satisfy the probabilistic threshold requirement; and 2) a distributed top-k-queries, optimizing, the transfer of the tuples from the distributed sources to the coordinator site and the time treatment. Extensive experiments are conducted to verify the effectiveness and efficiency of the proposed method in terms of communication costs and response time.

High performance location-based services in a main-memory database

With the widespread adoption of mobile devices and explosive growth of spatio-temporal data, Location-Based Services (LBS) have become an indispensable technology in our daily lives. The key characteristics of the LBS applications include a high rate of time-stamped location updates, and many concurrent historical, present and predictive queries. The commercial providers of LBS must support all three kinds of queries and address the high update rates. While they employ relational databases for this purpose, traditional databases are unable to cope with the growing demands of many LBS systems. Support for spatio-temporal indexes within these databases are limited to R-tree based approaches. Although a number of advanced spatiotemporal indexes have been proposed by the research community, only a few of them support historical queries. These indexing techniques, with support for historical queries, are unable to sustain high update and query throughput typical in LBS. Technological trends involving increasingly large main memory and growing processing core count offer opportunities to address some of these issues. We present several key ideas to support high performance commercial LBS by exploiting in-memory database techniques. Taking advantage of very large memory available in modern machines, our system maintains the location data and index for the past N days in memory. Older data and index are kept in disk. We propose an in-memory storage organization for high insert performance. We also introduce a novel spatio-temporal index that maintains partial temporal indexes in a versioned grid structure. The partial temporal indexes are organized as compressed bitmaps. With extensive evaluation, we demonstrate that our system supports high insert and query throughputs and it outperforms the leading LBS system by a significant margin.

TOWARDS A NORMALISED 3D GEOVISUALISATION: THE VIEWPOINT MANAGEMENT

Abstract. This paper deals with the viewpoint management in 3D environments considering an allocentric environment. The recent advances in computer sciences and the growing number of affordable remote sensors lead to impressive improvements in the 3D visualisation. Despite some research relating to the analysis of visual variables used in 3D environments, we notice that it lacks a real standardisation of 3D representation rules. In this paper we study the “viewpoint” as being the first considered parameter for a normalised visualisation of 3D data. Unlike in a 2D environment, the viewing direction is not only fixed in a top down direction in 3D. A non-optimal camera location means a poor 3D representation in terms of relayed information. Based on this statement we propose a model based on the analysis of the computational display pixels that determines a viewpoint maximising the relayed information according to one kind of query. We developed an OpenGL prototype working on screen pixels that allows to determine the optimal camera location based on a screen pixels colour algorithm. The viewpoint management constitutes a first step towards a normalised 3D geovisualisation.

Popularity-aware spatial keyword search on activity trajectories

The proliferation of GPS-enabled smart mobile devices enables us to collect a large-scale trajectories of moving objects with GPS tags. While the raw trajectories that only consists of positional information have been studied extensively, many recent works have been focusing on enriching the raw trajectories with semantic knowledge. The resulting data, called activity trajectories, embed the information about behaviors of the moving objects and support a variety of applications for better quality of services. In this paper, we propose a Top-k Spatial Keyword (TkSK) query for activity trajectories, with the objective to find a set of trajectories that are not only close geographically but also meet the requirements of the query semantically. Such kind of query can deliver more informative results than existing spatial keyword queries for static objects, since activity trajectories are able to reflect the popularity of user activities and reveal preferable combinations of facilities. However, it is a challenging task to answer this query efficiently due to the inherent difficulties in indexing trajectories as well as the new complexity introduced by the textual dimension. In this work, we provide a comprehensive solution, including the novel similarity function, hybrid indexing structure, efficient search algorithm and further optimizations. Extensive empirical studies on real trajectory set have demonstrated the scalability of our proposed solution.

Enhancing web search with queries of equivalent intents

Users often issue all kinds of queries to look for the same target due to the intrinsic ambiguity and flexibility of natural languages. Some previous work clusters queries based on co-clicks; however, the intents of queries in one cluster are not that similar but roughly related. It is desirable to conduct automatic mining of queries with equivalent intents from a large scale search logs. In this paper, we take account of similarities between query strings. There are two issues associated with such similarities: it is too costly to compare any pair of queries in large scale search logs, and two queries with a similar formulation, such as "SVN" (Apache Subversion) and support vector machine (SVM), are not necessarily similar in their intents. To address these issues, we propose using the similarities of query strings above the co-click based clustering results. Our method improves precision over the co-click based clustering method (lifting precision from 0.37 to 0.62), and outperforms a commercial search engine's query alteration (lifting $$F_1$$F1 measure from 0.42 to 0.56). As an application, we consider web document retrieval. We aggregate similar queries' click-throughs with the query's click-throughs and evaluate them on a large scale dataset. Experimental results indicate that our proposed method significantly outperforms the baseline method of using a query's own click-throughs in all metrics.

P066 Next-generation sequencing (NGS) HLA typing: Beyond allele assignment

Aim To expand the analytical potential of NGS in HLA typing. Studies based on SNP analysis have revealed the DNA sequence of HLA genes to carry important biological information typically overlooked in the simple exercise of allele assignment. NGS software is optimised to match sample sequences against databases of HLA allele sequence references. The goal here, however, is to create a method to open the data collected by NGS to any kind of query. Methods BLAST is still one of the most robust and efficient sequence-matching and sequence-alignment methods. Typically BAST databases are built with reference sequences against which a sample sequence is queried for matches. Here we reverse the approach and build a database of sample sequences against which we query for matches for particular sequences of interest following these steps: 1. Compile a database of sample sequences included in a given study. 2. Convert sample sequences to FASTA format. 3. Create a BLAST database with the study sample sequences. 4. Run any BLAST query against the BLAST database of the study sample sequences. Results This method was implemented using a dataset collected for typing purposes. The HLA-C 3’UTR polymorphisms which bind to miRNAs and regulate the level of expression (Nature 2011; 472:7344) were used. The BLAST output provided accurate information about which sequences carried the query polymorphism, which matched what is known about the association of these SNPs with HLA-C alleles. This method has also been successfully used to look for mutations that render HLA alleles null, and other SNP analysis queries. Conclusions NGS provides data that goes beyond the need for simple allele assignment. The method presented here provides (1) a robust and reliable way to store this accumulated information, (2) a quick and simple way to query this database of sequence data; and (3) an open method to ask any sequence question. M. Li: Employee; Company/Organization; Immucor.

Dynamic influence analysis in evolving networks

We propose the first real-time fully-dynamic index data structure designed for influence analysis on evolving networks. With this aim, we carefully redesign the data structure of the state-of-the-art sketching method introduced by Borgs et al. , and construct corresponding update algorithms. Using this index, we present algorithms for two kinds of queries, influence estimation and influence maximization , which are strongly motivated by practical applications, such as viral marketing. We provide a thorough theoretical analysis, which guarantees the non-degeneracy of the solution accuracy after an arbitrary number of updates. Furthermore, we introduce a reachability-tree-based technique and a skipping method , which greatly reduce the time consumption required for edge/vertex deletions and vertex additions, respectively, and counter-based random number generators , which improve the space efficiency. Experimental evaluations using real dynamic networks with tens of millions of edges demonstrate the efficiency, scalability, and accuracy of our proposed indexing scheme. Specifically, it can reflect a graph modification within a time of several orders of magnitude smaller than that required to reconstruct an index from scratch, estimate the influence spread of a vertex set accurately within a millisecond, and select highly influential vertices at least ten times faster than state-of-the-art static algorithms.

Monochromatic and bichromatic reverse top-k group nearest neighbor queries

The Group Nearest Neighbor (GNN) search is an important approach for expert and intelligent systems, i.e., Geographic Information System (GIS) and Decision Support System (DSS). However, traditional GNN search starts from users’ perspective and selects the locations or objects that users like. Such applications fail to help the managers since they do not provide managerial insights. In this paper, we focus on solving the problem from the managers’ perspective. In particular, we propose a novel GNN query, namely, the reverse top-k group nearest neighbor (RkGNN) query which returns k groups of data objects so that each group has the query object q as their group nearest neighbor (GNN). This query is an important tool for decision support, e.g., location-based service, product data analysis, trip planning, and disaster management because it provides data analysts an intuitive way for finding significant groups of data objects with respect to q. Despite their importance, this kind of queries has not received adequate attention from the research community and it is a challenging task to efficiently answer the RkGNN queries. To this end, we first formalize the reverse top-k group nearest neighbor query in both monochromatic and bichromatic cases, and then propose effective pruning methods, i.e., sorting and threshold pruning, MBR property pruning, and window pruning, to reduce the search space during the RkGNN query processing. Furthermore, we improve the performance by employing the reuse heap technique. As an extension to the RkGNN query, we also study an interesting variant of the RkGNN query, namely a constrained reverse top-k group nearest neighbor (CRkGN) query. Extensive experiments using synthetic and real datasets demonstrate the efficiency and effectiveness of our approaches.

Динамическая компиляция выражений в SQL-запросах для СУБД PostgreSQL

In recent years, as performance and capacity of main and external memory grow, performance of database management systems (DBMSes) on certain kinds of queries is more determined by raw CPU speed. Currently, PostgreSQL uses the interpreter to execute SQL queries. This yields an overhead caused by indirect calls to handler functions and runtime checks, which could be avoided if the query were compiled into native code on-the-fly, i.e. just-in-time (JIT) compiled: at run time the specific table structure is known as well as data types and built-in functions used in the query as well as the query itself. This is especially important for complex queries, performance of which is CPU-bound. We’ve developed a PostgreSQL extension that implements SQL query JIT compilation using LLVM compiler infrastructure. In this paper we show how to implement JIT compilation to speed up sequential scan operator (SeqScan) as well as expressions in WHERE clauses. We describe some important optimizations that are possible only with dynamic compilation, such as precomputing tuple attributes offsets only for attributes used by the query. We also discuss the maintainability of our extension, i.e. the automation for translating PostgreSQL backend functions into LLVM IR, using the same source code both for our JIT compiler and the existing interpreter. Currently, with LLVM JIT we achieve up to 5x speedup on synthetic tests as compared to original PostgreSQL interpreter.

Динамическая компиляция SQL-запросов для СУБД PostgreSQL

In recent years, as performance and capacity of main and external memory grow, performance of database management systems (DBMSes) on certain kinds of queries is more determined by raw CPU speed. Currently, PostgreSQL uses the interpreter to execute SQL queries. This yields an overhead caused by indirect calls to handler functions and runtime checks, which could be avoided if the query were compiled into native code on-the-fly, i.e. just-in-time (JIT) compiled: at run time the specific table structure is known as well as data types and built-in functions used in the query as well as the query itself. This is especially important for complex queries, performance of which is CPU-bound. We have developed a PostgreSQL extension that implements SQL query JIT compilation using LLVM compiler infrastructure. In this paper we show how to implement LLVM-analogues of the main operators of the PostgreSQL, how to replace Volcano iterator model abstraction (open(), next(), close()) by the abstraction that is more suitable to generate code for a particular query. Currently, with LLVM JIT we achieve up to 4.3x speedup on TPC-H Q1 query as compared to original PostgreSQL interpreter.

Abductive Conjunctive Query Answering w.r.t. Ontologies

In this article we investigate abductive conjunctive query answering w.r.t. ontologies and show how use cases can benefit from this kind of query answering service. While practical reasoning systems such as Racer have supported abductive conjunctive query answering for 10 years now, and many projects have exploited this feature, few publications deal with A-box abduction from an implementation perspective. This article gives a generalized overview on features provided by practical systems and also explains optimization techniques needed to meet practical requirements.

Towards ontology-based retrieval of historical images

A huge number of historical documents have been digitized over the last ten years. Browsing into these collections can be done using query by keywords or query by example systems. Going from one kind of query to another raises the problem of the semantic gap. In order to deal with this problem, this paper presents an ontology-based approach to the resolution of the semantic gap problem that uses inference rules with historical images. To do this, historians' knowledge and knowledge from the document processing domain were modeled using dedicated ontologies. Then, links between the regions of interest from the computer vision algorithms on the one hand, and their meaning on the other hand, were automatically created. These links will subsequently be used to help historians retrieve similar images. Based on the three ontologies defined and combined in this approach, we have defined rules to automatically annotate an image (to define the background for example) or a part of an image (to identify a letter, a body-part, ...) .

Bonding vertex sets over distributed graph

Given two sets of vertices in a graph, it is often of a great interest to find out how these vertices are connected, especially to identify the vertices of high prominence defined on the topological structure. In this work, we formally define a V ertex S et B onding query (shorted as VSB), which returns a minimum set of vertices with the maximum importance w.r.t total betweenness and shortest path reachability in connecting two sets of input vertices. We find that such a kind of query is representative and could be widely applied in many real world scenarios, e.g., logistic planning, social community bonding and etc. Challenges are that many of such applications are constructed on graphs that are too large to fit in single server, and the VSB query evaluation turns to be NP-hard. To cope with the scalability issue and return the near optimal result in almost real time, we propose a generic solution framework on a shared nothing distributed environment. With the development of two novel techniques, guided graph exploration and betweenness ranking on exploration, we are able to efficiently evaluate queries for error bounded results with bounded space cost. We demonstrate the effectiveness of our solution with extensive experiments over both real and synthetic large graphs on the Google's Cloud platform. Comparing to the exploration only baseline method, our method achieves several times of speedup.

Health, Food and User's Profile Ontologies for Personalized Information Retrieval

As we aim to retrieve personalized information to user's queries related to food, health and nutrition domains such as “Is apple good for people with heart diseases?”, “How much honey can be taken by a diabetic patient?”, “What are the health benefits of eating pineapple?” and “What are the fruits that contain the daily need quantity of calcium?” The information retrieval system needs to integrate ontologies from different domains such as food, nutrition, health (diseases, body parts, body functions) and recipe in order to answer such kind of queries. In addition, to support multilingual queries, the system and ontologies require aggregation of information from multi-level ontologies. Also, to achieve high relevancy and coverage we need to use ontologies that have comprehensive and rich vocabularies. Moreover, to make effective use for the annotation, ontologies concept names should be unique and self-contained. The main focus of this paper is to integrate ontologies from food, health and nutrition domains to help the personalized information systems to retrieve food and heath recommendations based on the user's health conditions and food preferences. Such ontologies that satisfy these requirements do not explicitly exist. Therefore, we were challenged to develop these ontologies by creating, integrating and reusing some of the existing ontologies to meet our requirements.

Locating Valid SLCAs for XML Keyword Search with NOT Semantics

Keyword search provides an easy way for users to pose queries against XML documents, and it is important to support queries with arbitrary combinations of AND, OR, and NOT operators. The previous RELMN algorithm processed such kind of queries by extending the original SLCA definition in a straightforward way, but it did not work correctly in some cases. In this paper, we propose the concept of valid SLCAs as query results. Basically, nodes in an XML document are classified according to their usages, which is further used to define the scope affected by a negative keyword. Only valid nodes, which are not affected by any negative keyword, are qualified to identify valid SLCAs. The experimental results show that the proposed algorithm achieves higher precision and recall, and is more efficient than the previous work.

Knowledge System Development Based on the Primitives of Sheet Metal

Through the sheet metal digital information classification, induces the 2D graphics, 3D model, data forms, the information such as process design chart, file information is automatic extracted and meta data description information is interactively supplemented , data is stored in the database records, two kinds of query is designed for recorded data query and keywords retrieval methods.Engineering database system designed to define meta data, storage, retrieval, information coding and classification of four levels, combined with the engineering application of system requirements and function module design,interface of sheet metal unfolding system is developed.The system improve the design capability of sheet metal enterprises, reduce manufacturing cost, enhance the capacity of information management .

A SURVEY ON LOCATION BASED SERACH USING SPATIAL INVERTED INDEX METHOD

Conventional spatial queries, nearest neighbor retrieval and vary search consists solely conditions on objects geometric property. But today, several fashionable applications support new kind of queries that aim to seek out objects that satisfies each spatial knowledge and their associated text. As an example instead of considering all the hotels, a nearest neighbor queries would instead elicit the building that's nearest to among people who offer services like pool, internet at a similar time. For this a sort of questioning a variant of inverted index is employed that's effective for multidimensional points associate degreed come with an Rtree which is constructed on each inverted list, and uses the algorithm of minimum bounding methodology which will answer the closest neighbor queries with keywords in real time.