A method for improving graph queries processing using positional inverted index (P.I.I) idea in search engines and parallelization techniques

The idea of positional inverted index is exploited for indexing of graph database. The main idea is the use of hashing tables in order to prune a considerable portion of graph database that cannot contain the answer set. These tables are implemented using column-based techniques and are used to store graphs of database, frequent sub-graphs and the neighborhood of nodes. In order to exact checking of remaining graphs, the vertex invariant is used for isomorphism test which can be parallel implemented. The results of evaluation indicate that proposed method outperforms existing methods.

Semantic-based query processing for relational data integration

To solve the query processing correctness problem for semantic-based relational data integration,the semantics of SAPRQL（simple protocol and RDF query language） queries is defined.In the course of query rewriting,all relative tables are found and decomposed into minimal connectable units.Minimal connectable units are joined according to semantic queries to produce the semantically correct query plans.Algorithms for query rewriting and transforming are presented.Computational complexity of the algorithms is discussed.Under the worst case,the query decomposing algorithm can be finished in O（n2） time and the query rewriting algorithm requires O（nm） time.And the performance of the algorithms is verified by experiments,and experimental results show that when the length of query is less than 8,the query processing algorithms can provide satisfactory performance.

Learned Distributed Query Optimizer:Architecture and Challenges

The query processing in distributed database management systems(DBMS)faces more challenges,such as more operators,and more factors in cost models and meta-data,than that in a single-node DMBS,in which query optimization is already an NP-hard problem.Learned query optimizers(mainly in the single-node DBMS)receive attention due to its capability to capture data distributions and flexible ways to avoid hard-craft rules in refinement and adaptation to new hardware.In this paper,we focus on extensions of learned query optimizers to distributed DBMSs.Specifically,we propose one possible but general architecture of the learned query optimizer in the distributed context and highlight differences from the learned optimizer in the single-node ones.In addition,we discuss the challenges and possible solutions.

An Optimized Labeling Scheme for Reachability Queries

Answering reachability queries is one of the fundamental graph operations.Existing approaches either accelerate index construction by constructing an index that covers only partial reachability relationship,which may result in performing cost traversing operation when answering a query;or accelerate query answering by constructing an index covering the complete reachability relationship,which may be inefficient due to comparing the complete node labels.We propose a novel labeling scheme,which covers the complete reachability relationship,to accelerate reachability queries processing.The idea is to decompose the given directed acyclic graph(DAG)G into two subgraphs,G1 and G2.For G1,we propose to use topological labels consisting of two integers to answer all reachability queries.For G2,we construct 2-hop labels as existing methods do to answer queries that cannot be answered by topological labels.The benefits of our method lie in two aspects.On one hand,our method does not need to perform the cost traversing operation when answering queries.On the other hand,our method can quickly answer most queries in constant time without comparing the whole node labels.We confirm the efficiency of our approaches by extensive experimental studies using 20 real datasets.

Processing Constrained K Closest Pairs Query in Spatial Databases

In this paper, constrained K closest pairs query is introduced, wbich retrieves the K closest pairs satisfying the given spatial constraint from two datasets. For data sets indexed by R trees in spatial databases, three algorithms are presented for answering this kind of query. Among of them, two-phase Range＋Join and Join＋Range algorithms adopt the strategy that changes the execution order of range and closest pairs queries, and constrained heap-based algorithm utilizes extended distance functions to prune search space and minimize the pruning distance. Experimental results show that constrained heap-base algorithm has better applicability and performance than two-phase algorithms.

Storage optimization for query processing over data streams

A defining characteristic of continuous queries over on-line data streams,possibly bounded by sliding windows,is the potentially infinite and time-evolving nature of their inputs and outputs.For different update patterns of continuous queries,suitable data structures bring great query processing efficiency.In this paper,we proposed a data structure suitable for weak nonmonotonic update pattern in which the lifetime of each tuple is known at generation time,but the length of lifetime is not necessarily the same.The new data structure combined the ladder queue with the feature of weak non-monotonic update pattern.The experiment results show that the new data structure performs much better than the traditional calendar queue in many cases.

Monitoring Median Queries over Moving Objects

The k-median problem has attracted a number of researchers. However,few of them have considered both the dynamic environment and the issue of accuracy. In this paper,a new type of query is studied,called continuous median monitoring （CMM） query. It considers the k-median problem under dynamic environment with an accuracy guarantee. A continuous group nearest neighbor based （CGB） algorithm and an average distance medoid （ADM） algorithm are proposed to solve the CMM problem. ADM is a hill climbing schemed algorithm and achieves a rapid converging speed by checking only qualified candidates. Experiments show that ADM is more efficient than CGB and outperforms the classical PAM （partitioning around medoids） and CLARANS （clustering large applications based on randomized search） algorithms with various parameter settings.

Answering reachability queries with ordered label constraints over labeled graphs

Reachability query plays a vital role in many graph analysis tasks.Previous researches proposed many methods to efficiently answer reachability queries between vertex pairs.Since many real graphs are labeled graph,it highly demands Label-Constrained Reachability(LCR)query inwhich constraint includes a set of labels besides vertex pairs.Recent researches proposed several methods for answering some LCR queries which require appearance of some labels specified in constraints in the path.Besides that constraint may be a label set,query constraint may be ordered labels,namely OLCR(Ordered-Label-Constrained Reachability)queries which retrieve paths matching a sequence of labels.Currently,no solutions are available for OLCR.Here,we propose DHL,a novel bloom filter based indexing technique for answering OLCR queries.DHL can be used to check reachability between vertex pairs.If the answers are not no,then constrained DFS is performed.So,we employ DHL followed by performing constrained DFS to answer OLCR queries.We show that DHL has a bounded false positive rate,and it's powerful in saving indexing time and space.Extensive experiments on 10 real-life graphs and 12 synthetic graphs demonstrate that DHL achieves about 4.8-22.5 times smaller index space and 4.6-114 times less index construction time than two state-of-art techniques for LCR queries,while achieving comparable query response time.The results also show that our algorithm can answer OLCR queries effectively.

Fast Web - Based Data Transmission

Since web based GIS processes large size spatial geographic information on internet, we should try to improve the efficiency of spatial data query processing and transmission. This paper presents two efficient methods for this purpose: division transmission and progressive transmission methods. In division transmission method, a map can be divided into several parts, called “tiles”, and only tiles can be transmitted at the request of a client. In progressive transmission method, a map can be split into several phase views based on the significance of vertices, and a server produces a target object and then transmits it progressively when this spatial object is requested from a client. In order to achieve these methods, the algorithms, “tile division”, “priority order estimation” and the strategies for data transmission are proposed in this paper, respectively. Compared with such traditional methods as “map total transmission” and “layer transmission”, the web based GIS data transmission, proposed in this paper, is advantageous in the increase of the data transmission efficiency by a great margin.

Hash-area-based data dissemination protocol in wireless sensor networks

HashQuery,a Hash-area-based data dissemination protocol,was designed in wireless sensor networks. Using a Hash function which uses time as the key,both mobile sinks and sensors can determine the same Hash area. The sensors can send the information about the events that they monitor to the Hash area and the mobile sinks need only to query that area instead of flooding among the whole network,and thus much energy can be saved. In addition,the location of the Hash area changes over time so as to balance the energy consumption in the whole network. Theoretical analysis shows that the proposed protocol can be energy-efficient and simulation studies further show that when there are 5 sources and 5 sinks in the network,it can save at least 50% energy compared with the existing two-tier data dissemination(TTDD) protocol,especially in large-scale wireless sensor networks.

Efficient processing of partially specified twig pattern queries

As huge volumes of data are organized or exported in tree-structured form, it is quite necessary to extract useful information from these data collections using effective and efficient query processing methods. A natural way of retrieving desired information from XML documents is using twig pattern （TP）, which is, actually, the core component of existing XML query languages. Twig pattern possesses the inherent feature that query nodes on the same path have concrete precedence relationships. It is this feature that makes it infeasible in many actual scenarios. This has driven the requirement of relaxing the complete specification of a twig pattern to express more flexible semantic constraints in a single query expression. In this paper, we focus on query evaluation of partially specified twig pattern （PSTP） queries, through which we can reap the most flexibility of specifying partial semantic constraints in a query expression. We propose an extension to XPath through introducing two Samepath axes to support partial semantic constraints in a concise but effective way. Then we propose a stack based algorithm, pTwigStack, to process a PSTP holistically without deriving the concrete twig patterns and then processing them one by one. Further, we propose two DTD schema based optimization methods to improve the performance of pTwigStack algorithm. Our experimental results on various datasets indicate that our method performs significantly better than existing ones when processing PSTPs.

Efficient Processing of Skyline Group Queries over a Data Stream

In this paper, we study the skyline group problem over a data stream. An object can dominate another object if it is not worse than the other object on all attributes and is better than the other object on at least one attribute. If an object cannot be dominated by any other object, it is a skyline object. The skyline group problem involves finding k-item groups that cannot be dominated by any other k-item group. Existing algorithms designed to find skyline groups can only process static data. However, data changes as a stream with time in many applications,and algorithms should be designed to support skyline group queries on dynamic data. In this paper, we propose new algorithms to find skyline groups over a data stream. We use data structures, namely a hash table, dominance graph, and matrix, to store dominance information and update results incrementally. We conduct experiments on synthetic datasets to evaluate the performance of the proposed algorithms. The experimental results show that our algorithms can efficiently find skyline groups over a data stream.

The Processing and Improvement of Multi-Statement Queries in Chiql

Chiql is a novel Chinese relational database query language for Chinese users. It supports procedural query style in which users can specify a complex database request in multiple simple statements. This facility renders Chiql simple-to-use and easy-toremember. However, direct execution of multi-statement Chiql queries (i.e. statement by statement) is often inefficient as potential indexbased operations (e.g. join) are by-passed. Furthermore, it often incurs additional database operations, such as scan and projection. To improve this situation, the SMA (Statement Merging Algorithm) is proposed. The goal of SMA is to merge as many dependent statements within a Chiql query as possible to form a more efficient Chiql query. The ability in achieving improved efficiency without sacrificing the simplicity of the language is the major advantage of this algorithm.

A Processing Approach for Event-Based Location Aware Queries in Hybrid Wireless Sensor Networks

In hybrid wireless sensor networks, sensor mobility causes the query areas to change dynamically. Aiming at the problem of inefficiency in processing the data aggregation queries in dynamic query areas, this paper proposes a processing approach for event-based location aware queries （ELAQ）, which includes query dissemination algorithm, maximum distance projection proxy selection algorithm, in-network query propagation, and aggregation algorithm. ELAQs are triggered by the events and the query results are dependent on mobile sensors＇ location, which are the characteristics of ELAQ model. The results show that compared with the TinyDB query processing approach, ELAQ processing approach increases the accuracy of the query result and decreases the query response time.

An Efficient Algorithm for Processing Multi-Relation Queries in Relational Databases

After a relation scheme R is decomposed into the set of schemes ρ={R_1,...,R_n},we may pose queries as if R existed in the database,taking a join of R_i's,when it is necessary to implement the query.Suppose a query involves a set of attributes S(?)R,we want to find the smallest subset of ρ whose union includes S.We prove that the problem is NP-complete and present a polynomial-bounded approximation algorithm.A subset of ρ whose union includes S and has a decomposition into 3NF with a lossless join and preservation of dependencies is given in the paper.

Automated Performance Tuning of Data Management Systems with Materializations and Indices

Automated performance tuning of data management systems offer various benefits such as improved performance, declined administration costs, and reduced workloads to database administrators (DBAs). Currently, DBAs tune the performance of database systems with a little help from the database servers. In this paper, we propose a new technique for automated performance tuning of data management systems. Firstly, we show how to use the periods of low workload time for performance improvements in the periods of high workload time. We demonstrate that extensions of a database system with materialised views and indices when a workload is low may contribute to better performance for a successive period of high workload. The paper proposes several online algorithms for continuous processing of estimated database workloads and for the discovery of the best plan for materialised view and index database extensions and of elimination of the extensions that are no longer needed. We present the results of experiments that show how the proposed automated performance tuning technique improves the overall performance of a data management system.

A Grammar Analysis Model for the Unified Multimedia Query Language

The unified multimedia query language （UMQL） is a powerful general-purpose multimedia query language, and it is very suitable for multimedia information retrieval. The paper proposes a grammar analysis model to implement an effective grammatical processing for the language. It separates the grammar analysis ofa UMQL query specification into two phases： syntactic analysis and semantic analysis, and then respectively uses Backus-Naur form （EBNF） and logical algebra to specify both restrictive grammar rules. As a result, the model can present error guiding information for a query specification which owns incorrect grammar. The model not only suits well the processing of UMQL queries, but aLso has a guiding significance for other projects concerning query processings of descriptive query languages.

k-Nearest Neighbor Query Processing Algorithms for a Query Region in Road Networks

Recent development of wireless communication technologies and the popularity of smart phones .are making location-based services （LBS） popular. However, requesting queries to LBS servers with users＇ exact locations may threat the privacy of users. Therefore, there have been many researches on generating a cloaked query region for user privacy protection. Consequently, an efficient query processing algorithm for a query region is required. So, in this paper, we propose k-nearest neighbor query （k-NN） processing algorithms for a query region in road networks. To efficiently retrieve k-NN points of interest （POIs）, we make use of the Island index. We also propose a method that generates an adaptive Island index to improve the query processing performance and storage usage. Finally, we show by our performance analysis that our k-NN query processing algorithms outperform the existing k-Range Nearest Neighbor （kRNN） algorithm in terms of network expansion cost and query processing time.

RPE Query Processing and Optimization Techniques for XML Databases

An extent join to compute path expressions containing parent-children andancestor-descendent operations and two path expression optimization rules, path-shortening andpath-complementing, are presented in this paper. Path-shortening reduces the number of joins byshortening the path while path-complementing optimizes the path execution by using an equivalentcomplementary path expression to compute the original one. Experimental results show that thealgorithms proposed are more efficient than traditional algorithms.

Effcient Distributed Skyline Queries for Mobile Applications

In this paper, we consider skyline queries in a mobile and distributed environment, where data objects are distributed in some sites （database servers） which are interconnected through a high-speed wired network, and queries are issued by mobile units （laptop, cell phone, etc.） which access the data objects of database servers by wireless channels. The inherent properties of mobile computing environment such as mobility, limited wireless bandwidth, frequent disconnection, make skyline queries more complicated. We show how to efficiently perform distributed skyline queries in a mobile environment and propose a skyline query processing approach, called efficient distributed skyline based on mobile computing （EDS-MC）. In EDS-MC, a distributed skyline query is decomposed into five processing phases and each phase is elaborately designed in order to reduce the network communication, network delay and query response time. We conduct extensive experiments in a simulated mobile database system, and the experimental results demonstrate the superiority of EDS-MC over other skyline query processing techniques on mobile computing.