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Skyline queries intend to retrieve the most superior data items in the database that best fit with the user's given preference. However, processing skyline queries are expensive and uneasy when applying on large distributed databases such as cloud databases. Moreover, it would be further sophisticated to process skyline queries if these distributed databases have missing values in certain dimensions. The effect of data incompleteness on skyline process is extremely severe because missing values result in un-hold the transitivity property of skyline technique and leads to the problem of cyclic dominance. This paper proposes an efficient model for computing skyline data items in cloud incomplete databases. The model focuses on processing skyline queries in cloud incomplete databases aiming at reducing the domination tests between data items, the processing time, and the amount of data transfer among the involved datacenters. Various set of experiments are conducted over two different types of datasets and the result demonstrates that the proposed solution outperforms the previous approaches in terms of domination tests, processing time, and amount of data transferred. [ABSTRACT FROM AUTHOR]

Support of immoderate preferred overall performance queries on huge volumes of spatial records has grown to be increasingly more important in lots of software domains, along with geospatial problems in several disciplines, vicinity based services. A significant elegance of longitudinal probes is connected to latitudinal grids (e.g. Street linkages and marine structures) because of this prevalence in a sizeable form of claims, at the side of intellectual conveyance systems, space-orientated totally services, and mobile workforce control. The presentation optimization of question dispensation in spatial networks specializes in diminishing community statistics entrances with the rate of system space calculations. In this paper intends algorithms aimed toward precise enough-NN queries, variety queries, closest-pair queries and also Many transport skyline queries, are part of queries primarily based mostly on a map reduce framework. It can employ worldwide and customizable question processing to obtain inexperienced common performance to address huge database. The advocate system has been verified thru giant experiments the use of actual and artificial datasets. The results we acquired are promising and display the performance of our processes in enhancing the overall performance of desire queries in disbursed and spatial databases. [ABSTRACT FROM AUTHOR]

Social Network Aggregators are used to maintain and manage manifold accounts over multiple online social networks. Displaying the Activity feed for each social network on a common dashboard has been the status quo of social aggregators for long, however retrieving the desired data from various social networks is a major concern. A user inputs the query desiring the specific outcome from the social networks. Since the intention of the query is solely known by user, therefore the output of the query may not be as per user's expectation unless the system considers 'user-centric' factors. Moreover, the quality of solution depends on these user-centric factors, the user inclination and the nature of the network as well. Thus, there is a need for a system that understands the user's intent serving structured objects. Further, choosing the best execution and optimal ranking functions is also a high priority concern. The current work finds motivation from the above requirements and thus proposes the design of a query processing system to retrieve information from social network that extracts user's intent from various social networks. For further improvements in the research the machine learning techniques are incorporated such as Latent Dirichlet Algorithm (LDA) and Ranking Algorithm to improve the query results and fetch the information using data mining techniques.The proposed framework uniquely contributes a user-centric query retrieval model based on natural language and it is worth mentioning that the proposed framework is efficient when compared on temporal metrics. The proposed Query Processing System to Retrieve Information from Social Network (QPSSN) will increase the discoverability of the user, helps the businesses to collaboratively execute promotions, determine new networks and people. It is an innovative approach to investigate the new aspects of social network. The proposed model offers a significant breakthrough scoring up to precision and recall respectively. [ABSTRACT FROM AUTHOR]

Rapid advancements of location-based information provided by publicly available GPS-enabled mobiles devices boost the generation of massive trajectory data. Recently, numerous researchers have addressed many problems regarding trajectory data, which is based on storage and queries processing. Further, a wide spectrum of application domains can benefit from trajectory data mining including trajectory organization as well as queries. Therefore, large-scale trajectory data has received increasing attention in research fields as well as in industry. As the massive trajectory data processing exceeds the power of centralized approaches used previously, in this paper, we survey various existing tools used to process large-scale trajectory data in a distributed way, e.g. MapReduce, Hadoop, and Spark. Furthermore, this paper reviews an extensive collection of existing applications of movement objects, including trajectory data mining and frequent trajectory. We also propose an open interesting research direction, which is challenging and has not been explored up until now, to improve the quality of trajectory query. [ABSTRACT FROM AUTHOR]

Incompleteness of data is a common problem in many databases including web heterogeneous databases, multirelational databases, spatial and temporal databases and data integration. The incompleteness of data introduces challenges in processing queries as providing accurate results that best meet the query conditions over incomplete database is not a trivial task. Several techniques have been proposed to process queries in incomplete database. Some of these techniques retrieve the query results based on the existing values rather than estimating the missing values. Such techniques are undesirable in many cases as the dimensions with missing values might be the important dimensions of the user's query. Besides, the output is incomplete and might not satisfy the user preferences. In this paper we propose an approach that estimates missing values in skylines to guide users in selecting the most appropriate skylines from the several candidate skylines. The approach utilizes the concept of mining attribute correlations to generate an Approximate Functional Dependencies (AFDs) that captured the relationships between the dimensions. Besides, identifying the strength of probability correlations to estimate the values. Then, the skylines with estimated values are ranked. By doing so, we ensure that the retrieved skylines are in the order of their estimated precision. [ABSTRACT FROM AUTHOR]

The advances in mobile device and wireless communication techniques have enabled anywhere, anytime information access. Information being accessed, whether structured or unstructured, can be classified into three categories: private data, shared data, and public data. Private and shared data are usually accessed through on-demand-based services, where user request is pushed to the server(s) and response to the request is generated and passed to the user (i.e., two way communication). Public data, on the other hand, can be most effectively disseminated using broadcasting technique (i.e., one way communication). In broadcasting, server(s) generates the broadcast contents and disseminate it through the air channel. The mobile user in search of public data, tunes to the air channel and pulls the desired information. The characteristics of mobile device and limitations of wireless communication technology pose challenges on broadcasting strategy as well as data retrieval algorithms. To reduce the access time and power consumption, major research issues include indexing techniques and data organization on air channel, broadcasting over single and parallel channel(s), data distribution and replication strategy, conflict resolution, and data retrieval methods This article is intended to articulate these challenges, propose several solutions, and through comprehensive simulation demonstrate the validity of the proposed solutions. [ABSTRACT FROM AUTHOR]

Querying nested data has become one of the most challenging issues for retrieving desired information from the Web. Today diverse applications generate a tremendous amount of data in different formats. These data and information exchanged on the Web are commonly expressed as nested representation such as XML, JSON, etc. Unlike the traditional database system, they do not possess a rigid schema. In general, the nested data is managed by storing data and its structures separately which significantly reduces the performance of data retrieving. Ensuring efficiency of processing queries which locates the exact positions of the elements has become a big challenging issue. There are different indexing structures which have been proposed in the literature to improve the performance of the query processing on the nested structure. Most of the past researches on nested structure concentrate on the structure alone. This paper proposes new index structure which combines siblings of the terminal nodes as one path which efficiently processes twig queries with less number of lookups and joins. The proposed approach is compared with some of the existing approaches. The results also show that they are processed with better performance compared to the existing ones. [ABSTRACT FROM AUTHOR]

Performing continuous skyline queries of dynamic data sets is now more challenging as the sizes of data sets increase and as they become more volatile due to the increase in dynamic updates. Although previous work proposed support for such queries, their efficiency was restricted to small data sets or uniformly distributed data sets. In a production database with many concurrent queries, the execution of continuous skyline queries impacts query performance due to update requirements to acquire exclusive locks, possibly blocking other query threads. Thus, the computational costs increase. In order to minimize computational requirements, we propose a method based on a multi-layer grid structure. First, relational data object, elements of an initial data set, are processed to obtain the corresponding multi-layer grid structure and the skyline influence regions over the data. Then, the dynamic data are processed only when they are identified within the skyline influence regions. Therefore, a large amount of computation can be pruned by adopting the proposed multi-layer grid structure. Using a variety of datasets, the performance evaluation confirms the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Research interest in skyline queries has been significantly increased over the years, as skyline queries can be utilized in many contemporary applications, such as multi-criteria decision-making system, decision support system, recommendation system, data mining, and personalized systems. Skyline queries return data item that is not dominated by any other data items in all dimensions (attributes). Most of the existing skyline approaches assumed that database is complete and values are present during the skyline process. However, such assumption is not always to be true, particularly in a real world database where values of data item might not be available (missing) in one or more dimensions. Thus, the incompleteness of the data impacts negatively on skyline process due to losing the transitivity property which leads into the issue of cyclic dominance. Therefore, applying skyline technique directly on an incomplete database is prohibitive and might result into exhaustive pairwise comparison. This paper presents an approach that efficiently evaluates skyline queries in incomplete database. The approach aims at reducing the number of pairwise comparisons and shortens the searching space in identifying the skylines. Several experiments have been conducted to demonstrate that our approach outperforms the previous approach through producing a lower number of pairwise comparisons. Furthermore, the result also illustrates that our approach is scalable and efficient. [ABSTRACT FROM AUTHOR]

The goal of query processing in WSN is to get reliable information of interest from sensor nodes whilst preserving, as much as possible, the network resources, mainly energy. Among the various approaches proposed in the literature to tackle this issue, serial approaches, in which the query is carried out serially from node to node, have shown noticeable improvements in terms of query processing responsiveness and communication overhead reduction when compared to centralized and distributed ones. Nevertheless, they suffer two main drawbacks: (a) they are intrinsically very vulnerable and (b) they require the construction of a Hamiltonian path through the network, which is known to be a NP-Complete problem. In this paper, we investigate the issue of efficient and robust query processing by proposing a novel approach, which we refer to as GBT (Greedy & Boundary Traversal). GBT is of a serialized and localized nature (i.e., each node does not maintain any knowledge about the topology of the network). Furthermore, in GBT the selection of the next hop is totally independent from the previous hops (i.e., no path is defined in advance). This feature enforces the robustness of GBT as attested by the simulation results we obtained (a mean improvement of almost 50% reduction in terms of communication, energy and query responsiveness in large-scale network topologies). We also provide a complexity analysis (time, space and communication) of our query processing algorithm as well as formal proof of its correctness (i.e., termination and completeness). [ABSTRACT FROM AUTHOR]

In multimedia information retrieval, multimedia data are represented as vectors in high-dimensional space. Performance of many indexing methods maintaining these vectors dramatically degrades with increasing dimensionality, which is known as the dimensionality curse. To resolve this dimensionality curse, dimensionality reduction methods which map vectors in high-dimensional space into those in low-dimensional space before the data are indexed have been proposed. This paper addresses indexing and searching issues for low-dimensional data vectors which are reduced by a dimensionality reduction method. This method maps high-dimensional vectors into low-dimensional ones by using their norms and approximated angles. However, in indexing such data using the R*-tree, which is a traditional multi-dimensional index structure, there several problems appear. In this paper, we first identify the problems associated w ith indexing and searching of low-dimensional vectors and then propose an approach to solve these problems. We formally prove that the proposed approach does not incur false dismissal in searching. Finally, we verify the superiority of the proposed approach via extensive experiments with synthetic and real-life data sets. [ABSTRACT FROM AUTHOR]

Constraints on the energy, bandwidth, and connectivity of mobile devices and wireless communication medium complicate the timely and reliable access to public data. Energy is often the most stringent constraint, necessitating techniques that facilitate operation in energy-saving modes. Broadcasting, typically over parallel channels, has proven to be an effective method for dissemination of public data to mobile devices. However, the employment of parallel channels introduces challenges associated with channel switching and conflicts due to concurrent accesses to multiple data items that ultimately increase energy consumption and response time. The detrimental effects on energy consumption and response time can be alleviated by scheduling the retrieval of data items in an order that reduces the number of passes over the air channels and channel switching between the parallel channels. In this paper, several scheduling algorithms are proposed and analyzed that achieve the aforementioned objectives. To further improve energy consumption and response time, the scope of our scheduling algorithms has been enhanced by replication of popular data items. The proposed scheduling algorithms, both with and without replication, have been simulated, and simulation results are presented and analyzed. These results show that the proposed scheduling algorithms, compared to some heuristic based methods, have greater impact in reducing energy consumption and response time. This reduction is shown to be more pronounced with replication of data items. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of fast processing of SPARQL queries on a large RDF dataset, where the RDF statements are quadruples (or quads). Quads can capture provenance or other relevant information about facts. This is especially powerful in modeling knowledge graphs, which are becoming increasingly important on the Web to provide high quality search results to users. We propose a new approach called RIQ that employs a decrease-and-conquer strategy for fast SPARQL query processing. Rather than indexing the entire RDF dataset, RIQ identifies groups of similar RDF graphs and creates indexes on each group separately. It employs a new vector representation for RDF graphs and locality sensitive hashing to construct the groups efficiently. It constructs a novel filtering index on the groups and compactly represents the index as a combination of Bloom and Counting Bloom Filters. During query processing, RIQ employs a streamlined approach. It constructs a query plan for a SPARQL query (containing one or more graph patterns), searches the filtering index to quickly identify candidate groups that may contain matches for the query, and rewrites the original query to produce an optimized query for each candidate. The optimized queries are then executed using an existing SPARQL processor that supports quads to produce the final results. We conducted a comprehensive evaluation of RIQ using a real and synthetic dataset, each containing about 1.4 billion quads. Our results show that RIQ can outperform its competitors designed to support named graph queries on RDF quads (e.g., Jena TDB and Virtuoso) for a variety of queries. [ABSTRACT FROM AUTHOR]

Compression is one of the most important techniques in data management, which is usually used to improve the query efficiency in database. However, there are some restrictions on existing compression algorithms that have been applied to numeric data in column-oriented databases. First, a compression algorithm is suitable only for columns with certain data distributions not for all kinds of data columns; second, a data column with irregular distribution is hard to be compressed; third, the data column compressed by using heavyweight methods cannot be operated before decompression which leads to inefficient query. Based on the fact that it is more possible for a column to have sub-regularity than have global-regularity, we developed a compression scheme called Vertically Partitioning Compression (VParC). This method is suitable for columns with different data distributions, even for irregular columns in some cases. The more important thing is that data compressed by VParC can be operated directly without decompression in advance. Details of the compression and query evaluation approaches are presented in this paper and the results of our experiments demonstrate the promising features of VParC. [ABSTRACT FROM AUTHOR]

With the evolution of technology, the ways to acquire data and the applications of data are more diverse. As data volume con- tinuously grows, the data quality may not be high as usual. The data can be defected, imprecise or inaccurate due to the process of data acquiring. Recently, the skyline query is widely used in data analysis to derive the results that meets more than one spe- cific condition simultaneously. For example, the forest monitoring system, which collects the temperature and humidity of the surrounding environment with sensors, to monitor the disasters. Using the skyline query, the zones of potential fire hazards can be found in time, where the temperature is high and the humidity is low. In the mentioned application, the derived data change with time. We refer to such data as data streams. The constant change and uncertainty of data make the query process difficult and need more computations. Thus, how to have an effective skyline query process in terms of time and space over uncertain data streams becomes crucial. In this paper, we discuss this problem and propose an effective approach, Efficient Probabilistic Skyline Update (EPSU), using a new data structure by augmenting the R-tree structure. The relevant algorithms are analyzed and discussed. Last, we perform the simulated experiments extensively with synthetic data to validate the EPSU approach. The results show that EPSU can effectively compute the probabilistic skyline query in terms of the time and space and outperforms the existing ones. [ABSTRACT FROM AUTHOR]

With the evolution of technology, the ways to acquire data and the applications of data are more diverse. As data volume con- tinuously grows, the data quality may not be high as usual. The data can be defected, imprecise or inaccurate due to the process of data acquiring. Recently, the skyline query is widely used in data analysis to derive the results that meets more than one spe- cific condition simultaneously. For example, the forest monitoring system, which collects the temperature and humidity of the surrounding environment with sensors, to monitor the disasters. Using the skyline query, the zones of potential fire hazards can be found in time, where the temperature is high and the humidity is low. In the mentioned application, the derived data change with time. We refer to such data as data streams. The constant change and uncertainty of data make the query process difficult and need more computations. Thus, how to have an effective skyline query process in terms of time and space over uncertain data streams becomes crucial. In this paper, we discuss this problem and propose an effective approach, Efficient Probabilistic Skyline Update (EPSU), using a new data structure by augmenting the R-tree structure. The relevant algorithms are analyzed and discussed. Last, we perform the simulated experiments extensively with synthetic data to validate the EPSU approach. The results show that EPSU can effectively compute the probabilistic skyline query in terms of the time and space and outperforms the existing ones. [ABSTRACT FROM AUTHOR]

For an XML database, it is important to automatically reason meaningful answers for a given keyword query. Particularly, the MaxMatch algorithm is proposed to output contributors, which represent more (or equal number of) keywords than those contained by their sibling nodes. Such concept can distinguish the importance of sibling nodes and has attracted a lot of attention. In this paper, we intend to improve the time and space performance of the original approach. We first propose the LeveiPrune algorithm, which only needs to construct the smallest necessary intermediate tree and can also reduce the times of processing nodes. We then extend the idea of constructing trees level by level to optimize physical representations, and design the corresponding algorithm LevelPrune+. According to the experimental results, our proposed algorithms outperform other state-of-the-art approaches by an order of magnitude in some cases. Moreover, the index size of LeveiPrune+ is significantly reduced compared with that of the original approach. [ABSTRACT FROM AUTHOR]

Peer-to-peer databases are becoming more prevalent on the internet for sharing and distributing applications, documents, files, and other digital media. The problem associated with answering large-scale ad hoc analysis queries, aggregation queries, on these databases poses unique challenges. This paper presents an extensive evaluation of database query processing model in peer-to-peer networks using top-k query processing technique and implemented by Java, and MySql. [ABSTRACT FROM AUTHOR]

Several algorithms for processing reverse skyline queries have been proposed in recent literature. However, these algorithms are based on pre-processing approaches, and hence involve complex procedures and waste storage space due to inefficient use of storage. In addition, they are not robust to frequently changing data as, they have to re-compute and update the pre-computed results. To overcome these issues, this paper proposes a novel algorithm to efficiently process reverse skyline queries using an approach based on two pruning methods: the search-area pruning method and the candidate-objects pruning method. Utilizing these pruning methods, the algorithm is able to process reverse skyline queries efficiently even in situations where data is changing frequently. The proposed algorithm also effectively reduces the inefficient use of storage under existing approaches for storing pre-computed results. We conducted extensive experiments to show that the proposed algorithm shows better performance compared to existing approaches regardless of the dimension, distribution, or size of the data. [ABSTRACT FROM AUTHOR]

Abstract: Context-aware computing is an exciting paradigm in which applications perceive and react to changing environments in an unattended manner. To enable behavioral adaptation, a context-aware application must dynamically acquire context data from different operating spaces in the real world, such as homes, shops and persons. Motivated by the sheer number and diversity of operating spaces, we propose a scalable context data management system in this paper to facilitate data acquisition from these spaces. In our system, we design a gateway framework for all operating spaces and develop matching algorithms to integrate the local context schemas of operating spaces into a global set of domain schemas upon which SQL-based context queries can be issued from applications. The system organizes the operating space gateways as peers in semantic overlay networks and employs distributed query processing techniques over these overlays. Evaluation results on a prototype implementation demonstrate the effectiveness of our system design. [Copyright &y& Elsevier]

Abstract: Preference query has received high interest due to its great benefits over various types of database applications. This type of query provides more flexible query operators that retrieve data items which are not dominated by the other data items in all attributes (dimensions). Many preference techniques for preference queries have been introduced including top-k, skyline, multi-objective skyline, top-k dominating, k-dominance, ranked skyline, and k-frequency. All of these preference techniques aimed at finding the “best” result that meets the user preferences. This paper aims at evaluating the performance of the five well-known preference evaluation techniques, namely: top-k, skyline, top-k dominating, k-dominance and k-frequency; in a real database application when high number of dimensions is the main concern. To achieve this, a recipe searching application with maximum number of 60 dimensions has been developed which assists users to identify the most desired recipes that fulfill their preferences. Several analyses have been carried out, where execution time is the main measurement used to evaluate each preference technique. [Copyright &y& Elsevier]

Grid systems are very useful platforms for distributed databases, especially in some situations in which the scale of data sources and user requests is very high. However, the main characteristics of grid systems such as dynamicity, large size and heterogeneity, bring new problems to the query processing domain such as resource discovery and resource allocation. In this paper, we provide a survey related to resource allocation methods for query processing in data grid systems. We provide a classification for existing studies considering their approaches to the resource allocation problem. We provide a synthesis of the studies and propose evaluations and comparisons for the different classes of studies. [ABSTRACT FROM AUTHOR]

Efficient query processing on XML data is an important task for querying the data web. In this paper, we consider the XML query which can be represented as a query tree with twig patterns, and also consists of complex full-text constraints. Two approaches are proposed. The structure-first approach will first identify the elements which satisfy the tag constraint, and then process the full-text constraint on the terms represented within each element. The satisfied elements will be combined to meet the complete twig constraints. On the other hand, the keyword-first approach will first identify the elements which represent the required keywords, and then return the elements which satisfy the given full-text predicates and structural constraints. We demonstrate, via an extensive experimental study, that the two approaches have their own merits. [ABSTRACT FROM AUTHOR]

Abstract: Linked Data is becoming the core part of modern Web applications and thus efficient access to structured information expressed in RDF gains paramount importance. A number of efficient local RDF stores exist already, while distributed indexing and distributed query processing over Linked Data with similar efficiency and data management features as known from traditional database and data integration systems are only starting to develop. Distributed approaches will necessarily co-exist with centralized schemes, as data will be owned by different stakeholders who may not want to provide their complete data sets to a central place. Additionally, central/integrated storage may be prohibited for organizational or legal reasons in certain areas. To support decentralized schemes, only a few attempts in this direction exist so far, but they are limited in terms of capabilities and the degree of distribution vs. efficiency, query expressivity, and scalability. To remedy this situation, the approach and proof-of-concept prototype presented in this paper provides a solution for these open challenges. As we argue for widely distributed systems as a possible answer to scalability issues, we first identify and discuss the main challenges and based on this analysis, we propose an approach for efficient and scalable query processing over distributed Linked Data sources, taking into account the latest advances in database technology. Our system is based on a layered architecture that makes use of the advantages of decentralized indexing and query processing approaches, which have been researched and matured over the last decade. Our approach is based on a logical algebra for queries over RDF data and a related physical query algebra to enable optimization, both on the logical and physical layers in query processing. The introduced operators and strategies for processing complex query plans make excessive use of parallelism and other optimization paradigms of distributed query processing. Our query processing framework includes a sophisticated cost model to enable cost-efficient query planning and query execution. We extensively evaluate our approach through an experimental evaluation of a real proof-of-concept deployment, which demonstrates the efficiency, applicability, and correctness of the proposed concepts. [Copyright &y& Elsevier]

Abstract: The explosive growth in the size of data centers, coupled with the wide-spread use of virtualization technology has brought power and energy consumption as major concerns for data center administrators. Provisioning decisions must take into consideration not only target application performance but also the power demands and total energy consumption incurred by the hardware and software. Failure to do so will result in damaged equipment, power outages, and ine_cient operation. Since database servers comprise one of the most popular and important server applications deployed in such facilities, it becomes necessary to have accurate cost models that can predict the power and energy demands that each database workloads will impose in the system. In this paper we present an empirical methodology to estimate the power and energy cost of database operations. Our methodology uses multiple-linear regression and factorial experimental design to derive accurate cost models that depend only on readily available statistics such as selectivity factors, tuple size, numbers columns and relational cardinality. Moreover, our method does not need measurement of individual hardware components, but rather total power and energy consumption measured at a server. We have implemented our methodology, and ran experiments with several server configurations. Our experiments indicate that we can predict power and energy more accurately than alternative methods. [Copyright &y& Elsevier]

With the advances in wireless communication technology and the advent of smartphones, research on location-based services (LBSs) is being actively carried out. In particular, several spatial index methods have been proposed to provide efficient LBSs. However, finding an optimal indexing method that balances query performance and index size remains a challenge in the case of wireless environments that have limited channel bandwidths and device resources (computational power, memory, and battery power). Thus, mechanisms that make existing spatial indexing techniques more efficient and highly applicable in resource-limited environments should be studied. Bitmap-based Spatial Indexing (BSI) has been designed to support LBSs, especially in wireless broadcast environments. However, the access latency in BSI is extremely large because of the large size of the bitmap, and this may lead to increases in the search time. In this paper, we introduce a Selective Bitmap-based Spatial Indexing (SBSI) technique. Then, we propose an Adaptive Bitmap-based Spatial Indexing (ABSI) to improve the tuning time in the proposed SBSI scheme. The ABSI is applied to the distribution of geographical objects in a grid by using the Hilbert curve (HC). With the information in the ABSI, grid cells that have no objects placed, (i.e., 0-bit information in the spatial bitmap index) are not tuned during a search. This leads to an improvement in the tuning time on the client side. We have carried out a performance evaluation and demonstrated that our SBSI and ABSI techniques outperform the existing bitmap-based DSI (B?DSI) technique. [ABSTRACT FROM AUTHOR]

Pervasive computing applications, such as video surveillance and robot control, involve diversified operations on physical devices. We call a sequence of operations on a device an action and study how to schedule real-time actions on the devices in pervasive computing. We identify a number of novel characteristics of this pervasive action scheduling problem and develop a dynamic, heuristic algorithm for the problem. The algorithm performs priority-based action scheduling whenever some device becomes free and does not reply on any system-defined scheduling interval. We have implemented our proposed action scheduling algorithm in a pervasive query processing system named Aorta and evaluated its performance using actions in a pervasive lab monitoring application. Our simulation results demonstrate the algorithm ensures small dropping rate of actions and has tiny computation cost. [ABSTRACT FROM AUTHOR]

Abstract: The RDF(S) data model has been proposed for encoding metadata about Web resources. As more and more Web resources are annotated using RDF(S), there is an urgent need for efficiently dealing with this large volume of data. In this paper, we present Atlas, a peer-to-peer system for storing, updating and querying RDF(S) data. The Atlas system has been built using the distributed hash table Bamboo. Atlas was developed in the context of project OntoGrid, where it was used as a distributed repository for RDF(S) metadata describing Grid services and resources. The development of Atlas continues in other projects that our group participates currently. This paper gives an overview of the most recent version of Atlas and discusses a representative application. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We have developed a configurable meta-search engine for the human resource domain which aims to aid job seekers in searching for job vacancies and to support meta-search providers in the rapid development of meta-search engines. A significant challenge in accessing heterogeneous, distributed information via meta-search engines is the schema/data matching and integration that is needed for resolving semantic heterogeneities between different source search engines. We adopt a hybrid approach, using multiple matching criteria and matchers. A domain-specific ontology serves as a global ontology. Mappings are derived between this and the source search engine interfaces. These mappings are used to generate an integrated meta-search query interface, support query processing in the meta-search engine, and resolve semantic conflicts arising during result extraction from the source search engines. Experiments conducted in the job search domain show that our hybrid approach increases the correctness of matching during the integration of source search interfaces. [ABSTRACT FROM AUTHOR]

Index structures that are based on sequence matching for XPath processing such as ViST, PRIX and LCS-TRIM have recently been proposed to reduce the search time of XML documents. However, ViST can cause a lot of unnecessary computation and I/O when processing structural joint queries because its numbering scheme is not optimized. PRIX and LCS-TRIM require much processing time for matching XML data trees and queries. In this paper, we propose a novel index structure that solves the problems of ViST and improves the performance of PRIX and LCS-TRIM. Our index structure provides the minimum sequence matching scheme to efficiently process structural queries. Finally, to verify the superiority of the proposed index structure with the minimum sequence matching scheme, we compare our index structure with ViST, PRIX and LCS-TRIM in terms of query processing of a single path or of a branching path including wild-cards ('*' and '//'). [ABSTRACT FROM AUTHOR]

Existing mobile navigators can readily display any static data related to the area surrounding a user. However, their ability to display any dynamic data is limited. In this paper, we enable the viewing of dynamic data on a navigator using wireless data broadcast. The dynamic data are periodically broadcast via base stations of wireless systems, and can be filtered by navigators fetching desired data. We address several crucial issues related to the design of this application, including data organizing, indexing, caching, and querying. We simulate the performance of the resulting system by measuring the time and energy costs associated with retrieving the dynamic data. [ABSTRACT FROM AUTHOR]

Abstract: This research is motivated by large-scale pervasive sensing applications. We examine the benefits and costs of caching data for such applications. We propose and evaluate several approaches to querying for, and then caching data in a sensor field data server. We show that for some application requirements (i.e., when delay drives data quality), policies that emulate cache hits by computing and returning approximate values for sensor data yield a simultaneous quality improvement and cost saving. This win–win is because when system delay is sufficiently important, the benefit to both query cost and data quality achieved by using approximate values outweighs the negative impact on quality due to the approximation. In contrast, when data accuracy drives quality, a linear trade-off between query cost and data quality emerges. We also identify caching and lookup policies for which the sensor field query rate is bounded when servicing an arbitrary workload of user queries. This upper bound is achieved by having multiple user queries share the cost of a single sensor field query. Finally, we demonstrate that our results are robust to the manner in which the environment being monitored changes using models for two different sensing systems. [Copyright &y& Elsevier]

The new generation of peer-to-peer applications requires advanced semantic search over distributed XML data sources over the internet. However, most current proposals in the literature only support simple XPath query answers in unstructured or structured P2P networks. In this paper, we present an effective and efficient framework to support complex XPath queries with full-text search predicates over schema-less XML data distributed in a OHT-based structured P2P network. We construct two types of routing data synopses, termed structural summary and peer-document synopsis, to route the user query to most relevant peers that own documents that can satisfy the query. To improve the search performance, our lazy query evaluation terminates as early as possible when the top-k query answers are retrieved. We also develop effective strategies to balance the data load distribution in the system. In addition, we introduce novel methods to handle network updates, such as node arrivals, departures, and failures. Finally, we conduct extensive experiments to show the scalability of our system, validate the efficiency and accuracy of our routing data synopses, and demonstrate the effectiveness of our load balancing schemes. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing interest in the database broadcasting system where the server periodically broadcasts contents of a database to mobile clients such as portable computers and PDAs. There are three query processing methods in the database broadcasting system: (i) the server processes a query and then broadcasts the query result to the client, (ii) the client stores all data that are necessary to process the query and then processes it locally, and (iii) the server and the client collaborate in processing the query. Since the performance of each method changes according to the situation such as the interval of query generation and the size of query results, it is difficult to choose the optimal method among them statically. In this paper, we propose new query processing methods which dynamically change the order of queries submitted in the queue at the server and also change processing methods for the queries according to the deadline of queries and the system situation. These methods improve the success rate as well as reduce the response time in most cases. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(2): 21– 31, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20689 [ABSTRACT FROM AUTHOR]

This paper discusses a schema integration and query processing method that enables integrated access to the multiple autonomous databases in the distributed environment with schemata restricted to a set of attributes. This method is based on the construction of a virtual database (VDB) formed over physically distributed component databases. Although user queries are issued to this VDB, these queries are decomposed to the subqueries using the virtual functional dependency (VFD), which is defined on the VDB and issued to each actual component database. The VDB- based method can obtain more answers that are logically valid than those gained from conventional query processing methods. When the VFD formed over a VDB is acycic among the nodes to which the query refers, sound and complete answers can be efficiently calculated. [ABSTRACT FROM AUTHOR]

This paper proposes the database model for the motion picture database, together with the query processing. The existing motion picture database is not convenient in describing the change of the scene attribute in the query condition to specify the subsequence of the motion picture database. Another more serious problem of the existing data model is that the desired sequence can never be retrieved from the scene sequence can never be retrieved from the scene sequence where the scene which does not satisfy the query condition is mixed or a scene is missing. To cope with the former problem, the regular expression which enables users to represent the iteration of the condition is introduced into the MeSOD model. Thus, the change of the scene attribute can be described by the regular expression for the condition. To cope with the latter problem, a query processing is defined formally (incomplete query processing), where the condition containing the regular expression is tolerated according to the database. As an actual example of the proposed approach, the broadcast of a Sumo program is considered, where the broadcast pattern changes during the course of a Sumo match. An adequate scene sequence is required to be extracted according to the condition representing a typical match. It is verified that an adequate scene sequence can be extracted by the incomplete query processing as a Sumo match from an actual Sumo broadcast program. The validity of the result of experiment is verified theoretically, and the effectiveness of the proposed approach is demonstrated. [ABSTRACT FROM AUTHOR]

This paper proposes a new join processing algorithm for the KD-tree indexed relations. The processing is evaluated by a simulation and the effectiveness is demonstrated. The KD-tree index is one of the typical multidimensional clustering data structures. A flexible database can be constructed which provides uniform access characteristics in regard to the multiple attributes. On the other hand, the data structure is made complex, and the traditional join processing technique for the ordinary one-dimensional clustering relation, which maintains the physical order, cannot be applied. Also, there has not been proposed a join technique for the KD-tree indexed relations. This paper introduces anew the concepts called "wave" and "join range," and proposes an efficient join algorithm for the KD-tree indexed relations. Then an extended algorithm is proposed by introducing the garbage collection function into the forementioned basic algorithm, where the unnecessary tuples on the main memory can be eliminated dynamically. Each of those algorithms is evaluated by a simulation, and the effectiveness is verified. When the order of the join attribute is not preserved, the join processing by the hash-based algorithm, which at present is considered the fastest, requires two read scannings and one write scanning, while the best algorithm proposed in this paper can execute the processing by almost one read scanning. Thus, a drastic improvement of the performance can be expected for the join processing by the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper describes new load-sharing algorithms for parallel database processing. There is a trade-off between overhead and load unbalance in ordinary algorithms. The proposed algorithms solve the tradeoff by varying the number of tasks allocated at one time, which is fixed in ordinary algorithms. Performance evaluations show that the proposed algorithms achieve fair load sharing with low overhead, independent of database size, the number of processors and data distribution. [ABSTRACT FROM AUTHOR]

Resource Description Framework (RDF) is increasingly being used to model data on the web. RDF model was designed to support easy representation and exchange of information on the web. RDF is queried using SPARQL, a standard query language recommended by W3C. The growth in acceptance of RDF format can be attributed to its flexible and reusable nature. The size of RDF data is steadily increasing as many government organizations and companies are using RDF for data representation and exchange. This resulted in the need for developing distributed RDF frameworks that can efficiently manage RDF data on large scale i.e. Big RDF data. These scalable distributed RDF data management systems competent enough to handle Big RDF data can also be termed as Big RDF frameworks. The proliferation of RDF data has made RDF data management a difficult task. In this survey, we provide an extensive literature on Big RDF frameworks from the aspect of storage, partitioning, indexing, query optimization and processing. A taxonomy of the tools and technologies used for storage and retrieval of Big RDF data in these systems has been presented. The comparative evaluation of some Big RDF frameworks based on query performance and our observations from this evaluation are described. The research challenges identified during the study of these systems are elaborated to suggest promising directions for future research. [ABSTRACT FROM AUTHOR]

Recently there is a research trend to integrate the cloud and the vehicular ad-hoc networks to enhance each other on the cooperative urban sensing applications. However, it remains a challenging problem on how to forward queries to search the results and then to route back the query results, as the vehicular nodes dynamically move on the road. Also, the dark side of vehicular cloud is often ignored. Getting the owner's identity or locations of a given vehicle, or getting the queries submitted by vehicles would put the data privacy at risk. In this paper we propose a privacy preserving query processing scheme for vehicular sensor networks. The scheme consists of two phases: 1) at the index mapping phase, sensed data are stored locally at road side units (RSU), and a global index is maintained at the cloud; 2) at the query processing phase, queries are transformed based on a mapping function, and then are routed to the cloud. The cloud computes the set of RSUs that have the query result based on the mapping index, and then directs the query to the RSUs. Then the result forwarding is transformed into a query result forwarding problem at the RSU, where the data of query results are routed back at their best paths either through the 4G channel or through the DSRC (Dedicated Short Range Communication) channel. Also, the query results are encrypted and diffused to a set of RSUs that the query requester would travel along, so the requester could fetch the results before the query is outdated. Extensive experiments demonstrate the effectiveness of the proposed algorithm in vehicular sensor networks. The ratio of successful query delivery is higher than existing query schemes, while at the same time preserving the privacy of query requesters and the data owner in vehicular sensor networks. [ABSTRACT FROM AUTHOR]

A technical infrastructure for storing, querying and managing RDF data is a key element in the current semantic web development. Systems like Jena, Sesame or the ICS-FORTH RDF Suite are widely used for building semantic web applications. Currently, none of these systems support the integrated querying of distributed RDF repositories. We consider this a major shortcoming since the semantic web is distributed by nature. In this paper we present an architecture for querying distributed RDF repositories by extending the existing Sesame system. We discuss the implications of our architecture and propose an index structure as well as algorithms for query processing and optimisation in such a distributed context. [ABSTRACT FROM AUTHOR]