Data management and query---Multiple range query optimization with distributed cache indexing

Multiple query scheduling for distributed semantic caches

In distributed query processing systems where caching infrastructure is distributed and scales with the number of servers, it is becoming more important to orchestrate and leverage a large number of cached objects in distributed caching systems seamlessly as the present trend is to build large scalable distributed systems by connecting small heterogeneous machines. With a large scale distributed caching system, a scheduling policy must consider both cache hit ratio and system load balance to optimize multiple queries. A scheduling policy that considers system load but not cache hit ratio often fails to reuse cached data by not assigning a query to the sever that has data objects the query needs. On the contrary, a scheduling policy that considers cache hit ratio but not system load balance may suffer from system load imbalance. To maximize the overall system throughput and to reduce query response time, a multiple query scheduling policy must balance system load and also leverage cached objects. In this paper, we present a distributed query processing framework that exhibits high cache hit ratio while achieving good system load balance. In order to seamlessly manage our distributed scalable caching system, our framework performs autonomic cached data migrations to improve cache hit ratio. Our experiments show that our proposed query scheduling policy and data migration policy significantly improve system throughput by achieving high cache hit ratio while avoiding system load imbalance.

The growth in the number of mobile users is motivating the new technology of mobile computing to offer many new types of services like location-based services that pose many challenging problems for location-dependent query processing and data management. In this paper, we discuss the various challenges in distributed processing of location-dependent continuous queries in a mobile environment by studying the different scenarios in which both the querying unit and the object being queried are in motion. Then we proceed to classify different types of location dependent queries. We discuss the different solutions to solve these complex location dependent queries using caching techniques that facilitate fast access to data and at the same time minimize the massive network traffic generated due to location dependent query processing in a wireless environment. To further reduce the data transfer the query results are cached so that these results are reusable. The conventional query processing method like join processing of data from mobile units and server is directly applied in the mobile environment. Additionally this paper also focuses on the methods of location-dependent query processing and applications. The main goal of this paper is to study the various location dependent query processing methods along with data management problems involved in evaluation of Location-dependent queries and classify them.

Query Processing is a key determinant in the overall performance of distributed databases. It requires processing of data at their respective sites and transmission of the same between them. These together constitute a distributed query processing strategy (DQP). DQP aims to arrive at an efficient query processing strategy for a given query. This strategy involves generation of efficient query plans for a distributed query. In case of distributed relational queries, the number of possible query plans grows exponentially with an increase in the number of relations accessed by the query. This number increases further when the relations, accessed by the query, have replicas at different sites. Such a large search space renders it infeasible to find optimal query plans. This paper presents a query plan generation algorithm that attempts to generate optimal query plans, for a given query, using genetic algorithm. The query plans so generated involve fewer sites, thus leading to efficient query processing. Further, experimental results show that the proposed algorithm converges quickly towards optimal query plans for an observed crossover and mutation probability.

A study on the influence of leadership orchestration on e-readiness accession in higher education in the Republic of Kenya concluded in 2018 showed that there is a correlation between the leadership performance on selected leadership indices and the institution’s digital age readiness. Does this correlation also apply to adoption of evidence-based decision-making and, thus, application of data analytics, visualization and business intelligence? Cross-sectional survey design was employed. Quantitative data was collected through simple random sampling using an online questionnaire in nine purposively selected universities. Seven key questions formulated around process and data were posed to the participants. Qualitative data was obtained using focus group discussion and interviews. Pearson Moment Co-efficient of Correlation and bivariate regression were done. Content analysis was applied on the qualitative data. Significant correlation was observed with six out of seven items. It is concluded that institutional perspective on process performance and evidence-based decision-making is correlated with leadership orchestration. Effective adoption and application of data analytics, visualization and business intelligence in the digital age is dependent on institutional leadership. Recommendation is made to re-orient leadership in higher education to evidence-based decision-making and data-driven culture in order to harness the power of data analytics, visualization and business intelligence to improve the quality and productivity of higher education in Kenya.

With the explosion of data in the past decade, big data is becoming a research hotspot in the information field. Many cloud-based distributed data processing platforms have been proposed to provide efficient and cost effective solutions for big data query processing, such as Hadoop, Hive, Pig, etc. However, most of the current research works are focus on improving the performance of query processing based on the view of systematics while without considering the characteristics of queries themselves, such as the query similarity, which will cause large numbers of redundant computation, effect query execution efficiency, thus having an adverse impact on promotion of the multi-queries processing performance. To solve this problem, in this paper, we propose a Multi-queries optimization framework based on MapReduce-oriented cloud environment (Multi-Q), which utilizes the dependence between multiple queries to realize query results reuse. Firstly, a cluster-based partition algorithm called CPA has been exploited to conduct the logic partition of the search range of query workload. Secondly, a multi-queries reuse dependence graph (MRDG) construction method on the basis of the cluster-based partition results has been presented to depict the dependence between the multiple queries. Finally, a Multi-Q processing algorithm based on Multi-Q Reuse Dependence Graph has been put forward to achieve the query results reuse and improve the overall query processing performance. We evaluate our approach by deploying Multi-Q based on Hadoop in a real cloud environment, called SEU-Cloud, and conducting extensive experiments based on the standard TPC-H. The result verifies that compared with Hive, the performance of improvement is approximately 39.3% by using our Multi-Q.

Database researchers have made significant progress on several research issues related to multidimensional data analysis, including the development of fast cubing algorithms, efficient schemes for creating and maintaining precomputed group-bys, and the design of efficient storage structures for multidimensional data. However, to date there has been little or no work on multidimensional query optimization. Recently, Microsoft has proposed “OLE DB for OLAP” as a standard multidimensional interface for databases. OLE DB for OLAP defines Multi-Dimensional Expressions (MDX), which have the interesting and challenging feature of allowing clients to ask several related dimensional queries in a single MDX expression. In this paper, we present three algorithms to optimize multiple related dimensional queries. Two of the algorithms focus on how to generate a global plan from several related local plans. The third algorithm focuses on generating a good global plan without first generating local plans. We also present three new query evaluation primitives that allow related query plans to share portions of their evaluation. Our initial performance results suggest that the exploitation of common subtask evaluation and global optimization can yield substantial performance improvements when relational database systems are used as data sources for multidimensional analysis.

Nowadays, with the constantly increasing amount of data, we are facing a growing number of users, who are characterized by a frequent and a massively concurrent data access. The large number of users pose multiple query optimization problems. In a distributed data warehousing system such as Hadoop/Hive, queries are evaluated one at a time and processed with the MapReduce paradigm. The massive query execution usually overloads and slows down the entire distributed environment mainly due to multiple data scan tasks. In this paper we aim to optimize the multiple query execution performance on Hive. We propose Shared Distributed Datasets (\( S2D \)), a method that dynamically looks for and shares common data among queries. The evaluation shows that, compared to Hive, \( S2D \) consumes on average 20% less memory in the Map-scan task and it is 12% faster regarding the execution time of interactive and reporting queries from TPC-DS.

A Reverse Top-k Geo-Social Keyword Query (RkGSKQ) aims to find all the users who have a given geo-social object in their top-k geo-social keyword query results. This query is practical in detecting prospective customers for online business in social networks. Existing work on RkGSKQ only explored efficient approaches in answering a single query per time, which could not be efficient in processing multiple queries in a query batch. In many real-life applications, multiple RkGSKQs for multiple query objects can be issued at the same time. To this end, in this paper, we focus on the efficient batch processing algorithm for multiple RkGSKQs. To reduce the overall cost and find concurrently results of multiple queries, we present a group processing framework based on the current state-of-the-art indexing and group pruning strategies to answer multiple RkGSKQs by sharing common CPU and I/O costs. Extensive experiments on three data sets demonstrate the effectiveness and efficiency of our proposed methods.

We propose the combination of techniques that solve multiple queries for motion planning problems with single query planners in a motion planning framework that can be efficiently parallelized. In multiple query motion planning, a data structure is built during a preprocessing phase in order to quickly respond to on-line queries. Alternatively, in single query planning, there is no preprocessing phase and all computations occur during query resolution. This paper shows how to effectively combine a powerful sample-based method primarily designed for multiple query planning (the Probabilistic Roadmap Method - PRM) with sample-based tree methods that were primarily designed for single query planning (such as Expansive Space Trees, Rapidly Exploring Random Trees, and others). Our planner, which we call the Probabilistic Roadmap of Trees (PRT), uses a tree algorithm as a subroutine for PRM. The nodes of the PRM roadmap are now trees. We take advantage of the very powerful sampling schemes of recent tree planners to populate our roadmaps. The combined sampling scheme is in the spirit of the non-uniform sampling and refinement techniques employed in earlier work on PRM. PRT not only achieves a smooth spectrum between multiple query and single query planning but it combines advantages of both. We present experiments which show that PRT is capable of solving problems that cannot be addressed efficiently with PRM or single-query planners. A key advantage of PRT is that it is significantly more decoupled than PRM and sample-based tree planners. Using this property, we designed and implemented a parallel version of PRT. Our experiments show that PRT distributes well and can easily solve high dimensional problems that exhaust resources available to single machines.

Communication-efficient processing of multiple continuous aggregate queries

Graph Analytics (GA) is a rapidly growing field that holds significant importance in numerous applications that focuses on analysing the complex relations in a network. One of the crucial concepts in GA is Query Reachability which refers to the ability to find a path connecting two nodes (u, v) in graph G. The multiple attributes in reachability query processing play a significant role to discover some interesting patterns. Existing studies did not deal with processing complex queries in attributed graphs. To fulfill some of the challenges, We contributed to this paper with three ideas for solving complex queries, finding paths, and processing multiple queries in parallel and distributed paradigms. Therefore, 1) Proposed Distributed Set Reachability Query in Attributed Networks (DSRQ_AN) algorithm using distributed computing through the Neo4j graph data platform and Cypher Query Language, 2) Finding the path to the given complex queries, and 3) Proposed a Multiple Query Processing in Parallel and Distributed (MQP_PD) algorithm to execute in a parallel environment. This paper assessed the performance of the proposed approaches through a parallel paradigm on Neo4j slaves. Furthermore, the demonstrated results of parallel execution outperform sequential execution for multiple queries on attributed graphs.

Peer-to-Peer infrastructures are emerging as one of the important data management infrastructures in the World Wide Web. So far, however, most work has focused on simple P2P networks which tackle efficient query distribution to a large set of peers but assume that each query can be answered completely at each peer. For queries which need data from more than one peer to be executed this is clearly insufficient. Unfortunately, though quite a few database techniques can be re-used in the P2P context, P2P data management infrastructures pose additional challenges caused by the dynamic nature of these networks. In P2P networks, we can assume neither global knowledge about data distribution, nor the suitableness of static topologies and static query plans for these networks. Unlike in traditional distributed database systems, we cannot assume complete information schema and allocation schema instances but rather work with distributed schema information which can only direct query processing tasks from one node to one or more neighboring nodes. In this paper we first describe briefly our super-peer based topology and schema-aware distributed routing indices extended with suitable statistics and describe how this information is extracted and updated. Second we show how these indices facilitate the distribution and dynamic expansion of query plans. Third we propose a set of transformation rules to optimize query plans and discuss different optimization strategies in detail, enabling efficient distributed query processing in a schema-based P2P network.

A private data federation enables clients to query the union of data from multiple data providers without revealing any extra private information to the client or any other data providers. Unfortunately, this strong end-to-end privacy guarantee requires cryptographic protocols that incur a significant performance overhead as high as 1,000 x compared to executing the same query in the clear. As a result, private data federations are impractical for common database workloads. This gap reveals the following key challenge in a private data federation: offering significantly fast and accurate query answers without compromising strong end-to-end privacy. To address this challenge, we propose SAQE, the Secure Approximate Query Evaluator, a private data federation system that scales to very large datasets by combining three techniques --- differential privacy, secure computation, and approximate query processing --- in a novel and principled way. First, SAQE adds novel secure sampling algorithms into the federation's query processing pipeline to speed up query workloads and to minimize the noise the system must inject into the query results to protect the privacy of the data. Second, we introduce a query planner that jointly optimizes the noise introduced by differential privacy with the sampling rates and resulting error bounds owing to approximate query processing. Our research shows that these three techniques are synergistic: sampling within certain accuracy bounds improves both query privacy and performance, meaning that SAQE executes over less data than existing techniques without sacrificing efficiency, privacy, or accuracy. Using our optimizer, we leverage this counter-intuitive result to identify an inflection point that maximizes all three criteria prior query evaluation. Experimentally, we show that this result enables SAQE to trade-off among these three criteria to scale its query processing to very large datasets with accuracy bounds dependent only on sample size, and not the raw data size.

Knowledge hypergraph-based approach for data integration and querying: Application to Earth Observation