Query Caching Research Articles

Optimizing the Performance of Data Warehouse by Query Cache Mechanism in Big Data

In today's world of Business Intelligence (BI), fast and efficient access to data from Data Warehouses (DW) is crucial. With the increasing amount of Big Data, caching has become one of the most effective techniques for improving data access performance. DWs are widely used by organizations for managing and using data in Decision Support Systems (DSS). To optimize the performance of fetching data from DWs, various methods have been employed, and one of them is the Query Cache method. Our proposed work focuses on a cache-based mechanism that improves the performance of DWs in two ways. First, it reduces the execution time by directly accessing records from cache memory, and second, it saves cache memory space by eliminating non-frequently used data. Our goal is to fill the cache memory with the most frequently used data. To achieve this objective, we utilize an aging-based Least Frequently Used (LFU) algorithm that considers the size and frequency of data simultaneously. This algorithm manages the priority and expiry age of the data in cache memory by taking into account both the size and frequency of data. LFU assigns priorities and counts the age of data placed in the cache memory. The cache block entry with the lowest age count and priority is eliminated first. Ultimately, our proposed cache mechanism efficiently utilizes cache memory and significantly improves the performance of data access between the main DW and the business user query

Optimizing the Performance of Data Warehouse by Query Cache Mechanism

Fast access of data from Data Warehouse (DW) is a need for today’s Business Intelligence (BI). In the era of Big Data, the cache is regarded as one of the most effective techniques to improve the performance of accessing data. DW has been widely used by several organizations to manage data and use it for Decision Support System (DSS). Many methods have been used to optimize the performance of fetching data from DW. Query cache method is one of those methods that play an effective role in optimization. The proposed work is based on a cache-based mechanism that helps DW in two aspects: the first one is to reduce the execution time by directly accessing records from cache memory, and the second is to save cache memory space by eliminating non-frequent data. Our target is to fill the cache memory with the most used data. To achieve this goal aging-based Least Frequently Used (LFU) algorithm is used by considering the size and frequency of data simultaneously. The priority and expiry age of the data in the cache memory is managed by dealing with both the size and frequency of data. LFU sets priorities and counts the age of data placed in cache memory. The entry with the lowest age count and priority is eliminated first from the cache block. Ultimately, the proposed cache mechanism efficiently utilized cache memory and fills a large performance gap between the main DW and the business user query.

Quantifying Location Privacy Risks Under Heterogeneous Correlations

Currently, increasingly ubiquitous location-based services are facilitating the activities of people in daily life. However, releasing real locations could lead to serious concerns about privacy. To remedy these issues, a number of location privacy protection mechanisms (LPPMs) have been proposed, e.g., spatial cloaking, dummy location generation, query caching, and perturbation. However, these LPPMs are vulnerable to inference attacks because of the incompleteness of the captured privacy risks caused by heterogeneous correlations in location data, e.g., semantical, temporal, and social correlations. Consequently, they cannot provide sufficient privacy guarantees due to the absence of embedded heterogeneous correlations in the design process of LPPM. To address these issues, we present QUAD, a framework for quantifying location privacy risks under heterogeneous correlations. QUAD has three features: 1) it enables the modeling and seamless fusion of multiple kinds of correlations that are available to adversaries; 2) it provides a probabilistic representation of the privacy risks faced under heterogeneous correlations; and 3) it achieves the quantification of privacy risks for multiple kinds of LPPMs that are widely used in the literature. To mitigate privacy threats, we propose a defense mechanism embedded with the quantified privacy risks. Extensive experiments on two real-world datasets confirm that QUAD can capture more privacy risks than competitors, and the risks can be dramatically reduced by our defense mechanism.

Enhanced query processing over semantic cache for cloud based relational databases

Semantic cache diminishes the expectancies of data retrieval over distributed system like cloud-based systems, by reusing already extracted data. It improves the performance of the retrieval system with limited bandwidth which is a key requirement in cloud, fog, edge and other mobile computing technologies. Cache management and query processing over semantic cache are two key activities which should be handled carefully. Query dispensation performs a significant part in terms of reusability in already retrieved data efficiently. Competent query dispensation algorithms made the semantic cache more efficient. This paper identifies four issues that cause decreasing the efficiency of query processing algorithm. The solution is provided to the identified issues to improve the efficiency of query processing. Improvement in efficiency is discussed along each of the issues. Efficiency analysis for enhanced algorithm is also provided and compared with state-of-the-art algorithms in the field. The proposed approach significantly improves the efficiency in many ways.

ICRA: index based cache replacement algorithm for cloud storage

Meteorological dataset is usually big and has a clear field. Due to its text form, each read and display will take up more system resources. In addition, the loading speed of the webpage also affects the user's online experience. According to these problems, we proposed a cache replacement algorithm combined with indexing algorithm called index cache replacement algorithm. The main innovation of this paper is that the index is created while reading the document and the page data at the first time and analyse, sort, and cache the indexing data at the same time. When users browse the same page, we can directly index file to query and return the corresponding data. Thus, it enhances the effectiveness of cached data and cache query and improves the query file and byte hit rate which finally improve the performance of the network.

ICRA: index based cache replacement algorithm for cloud storage

Meteorological dataset is usually big and has a clear field. Due to its text form, each read and display will take up more system resources. In addition, the loading speed of the webpage also affects the user's online experience. According to these problems, we proposed a cache replacement algorithm combined with indexing algorithm called index cache replacement algorithm. The main innovation of this paper is that the index is created while reading the document and the page data at the first time and analyse, sort, and cache the indexing data at the same time. When users browse the same page, we can directly index file to query and return the corresponding data. Thus, it enhances the effectiveness of cached data and cache query and improves the query file and byte hit rate which finally improve the performance of the network.

Quaestor

Today, web performance is primarily governed by round-trip latencies between end devices and cloud services. To improve performance, services need to minimize the delay of accessing data. In this paper, we propose a novel approach to low latency that relies on existing content delivery and web caching infrastructure. The main idea is to enable application-independent caching of query results and records with tunable consistency guarantees, in particular bounded staleness. Q uaestor (Query Store) employs two key concepts to incorporate both expiration-based and invalidation-based web caches: (1) an Expiring Bloom Filter data structure to indicate potentially stale data, and (2) statistically derived cache expiration times to maximize cache hit rates. Through a distributed query invalidation pipeline, changes to cached query results are detected in real-time. The proposed caching algorithms offer a new means for data-centric cloud services to trade latency against staleness bounds, e.g. in a database-as-a-service. Q uaestor is the core technology of the backend-as-a-service platform Baqend, a cloud service for low-latency websites. We provide empirical evidence for Q uaestor 's scalability and performance through both simulation and experiments. The results indicate that for read-heavy workloads, up to tenfold speed-ups can be achieved through Q uaestor 's caching.

Optimal hybrid broadcast scheduling and adaptive cooperative caching for spatial queries in road networks

Wireless broadcast is an efficient strategy for disseminating data to a large population of clients in road networks. Network Partition Index (NPI) does not address how to handle the environment effectively when the data objects are increased to a large number in broadcast system and physical restrictions of clients which include low bandwidth, mobility, limited energy and capacity. To address these problems, an NPI based air index, called Spatial Air Index (SAI) has been proposed by using Optimal Hybrid Broadcast (OHB) scheduling and Adaptive Cooperative Caching (ACC) for spatial queries in road networks. Experiments are conducted for evaluating query performance and comparison of SAI with state-of-the-art NPI. The experimental results show that SAI outperforms the rest.

Integration and optimization of multiple big data processing platforms

Purpose – The purpose of this paper is to integrate and optimize a multiple big data processing platform with the features of high performance, high availability and high scalability in big data environment. Design/methodology/approach – First, the integration of Apache Hive, Cloudera Impala and BDAS Shark make the platform support SQL-like query. Next, users can access a single interface and select the best performance of big data warehouse platform automatically by the proposed optimizer. Finally, the distributed memory storage system Memcached incorporated into the distributed file system, Apache HDFS, is employed for fast caching query results. Therefore, if users query the same SQL command, the same result responds rapidly from the cache system instead of suffering the repeated searches in a big data warehouse and taking a longer time to retrieve. Findings – As a result the proposed approach significantly improves the overall performance and dramatically reduces the search time as querying a database, especially applying for the high-repeatable SQL commands under multi-user mode. Research limitations/implications – Currently, Shark’s latest stable version 0.9.1 does not support the latest versions of Spark and Hive. In addition, this series of software only supports Oracle JDK7. Using Oracle JDK8 or Open JDK will cause serious errors, and some software will be unable to run. Practical implications – The problem with this system is that some blocks are missing when too many blocks are stored in one result (about 100,000 records). Another problem is that the sequential writing into In-memory cache wastes time. Originality/value – When the remaining memory capacity is 2 GB or less on each server, Impala and Shark will have a lot of page swapping, causing extremely low performance. When the data scale is larger, it may cause the JVM I/O exception and make the program crash. However, when the remaining memory capacity is sufficient, Shark is faster than Hive and Impala. Impala’s consumption of memory resources is between those of Shark and Hive. This amount of remaining memory is sufficient for Impala’s maximum performance. In this study, each server allocates 20 GB of memory for cluster computing and sets the amount of remaining memory as Level 1: 3 percent (0.6 GB), Level 2: 15 percent (3 GB) and Level 3: 75 percent (15 GB) as the critical points. The program automatically selects Hive when memory is less than 15 percent, Impala at 15 to 75 percent and Shark at more than 75 percent.

Efficient Caching and Mobility Adaptive Popular Data Access in Mobile Ad Hoc Networks

Background: Caching technique is widely used to cope up with the resource constraints and to improve the data accessibility in MANETs. The key constraints are reducing the data access time, overhead and energy cost. Methods: This work proposes a Popularity-driven Caching and Mobility adaptive query Searching (PCMS) to achieve the constraints. The PCMS consists of Cache Node selection, popularity and data size driven replacement (POP) algorithm and mobility adaptive searching for cache data. With the aim of reducing the access delay of popular data, the PCMS selects a node that is closer to the destination in a route to cache the frequently requested data item and serves the future requests. The PCMS takes into account the data popularity and size to decide caching and replacement policy and thus significantly improves the performance of caching. The mobility adaptive searching for cache data looks for an item in Query Directory (QD) nodes and redirects the request to the concerned caching node. It updates the cache information at QD by adhering to the mobility prediction scheme. Findings: The PCMS considers the data popularity and size as key factors to decide caching and replacement policy for the better utilization of cache memory. The PCMS has successfully reduced the network traffic and improves the Cache Hit Ratio by adhering the mobility prediction based update scheme. Applications/Improvements: The proposed system achieves appreciably better performance in simulation than the existing caching systems by utilizing minimum battery power, bandwidth and access delay.

Asynchronous data translation framework for converting relational tables to document stores

Although Not Only SQL (NoSQL) techniques have some new features such as query optimization and caching in the era of big data, it is not mature enough to replace traditional Relational Database Management System (RDBMS). In a long period of time, the hybrid solution of RDBMS and NoSQL is a main steam technology. In this paper, we present an asynchronous data framework to translate relational tables into schema-free document stores. The challenge of this framework is how to ensure that the translation is transparent and pervasive to the application with minimal impact on RDBMS performance. To better describe the translation process, we present the intermediate cell state model (CSM) to describe versioning incremental state. We address this challenges by translating the binary log of RDBMS to CSM repository, and the repository is reset to the NoSQL when encountering schema adjustment asynchronously. Finally, we leverage the CSM rectification algorithm and NoSQL synchronization algorithm to implement the whole framework. The experimental results illustrate that this translation has a less minimal impact on RDBMS performance and lower cost than other solutions.

The Implementation of Caching Database to Reduce Query’s Response Time

Performance is an imperative aspect of a web-based application. Response time is one of the most important performance parameters. Most of web-based applications are driven by database system. Reducing response time of its database system will improve the performance of a web-based application. Caching is one of techniques that can be implemented to reduce response time. The implementation of caching can be conducted in the application or database side. Caching is a technique to keep the executed query and hence the query will not be executed when called in the later execution. The result of an executed query will be stored in the cache therefore when that query is called, the database will retrieve the stored result without re-executing query. This mechanism will reduce the time to re-execute queries. However, this technique may be not suitable for all queries. This research is intended to investigate the effect of the implementation of caching into performance of a database. A series of experiments have been conducted by applying query cache. The response time of the application of query cache is compared with the response time of database without query. The results show that the implementation of query cache reduces response time significantly. However, this technique is more suitable for large database with many frequently asked queries. The number of clients accessing the database is found to be influencing the performance of caching. The more clients access the database, the more improvement is provided by query cache.

ECHO: Efficient Complex Query over DHT Overlays

In this article we propose ECHO, a novel and lightweight solution that efficiently supports range queries over a ring-like Distributed Hash Table (DHT) structure. By implementing a tree-based index structure and an effective query routing strategy, ECHO provides low-latency and low-overhead query searches by exploiting the Tabu Search principle. Load balancing is also improved reducing the traditional bottleneck problems arising in upper level nodes of tree-based index structures such as PHT. Furthermore, ECHO copes with DHT churn problems as its index exploits logical information as opposed to static reference cache approaches or replication techniques.The performance evaluation results obtained using PeerSim simulator show that ECHO achieves efficient performance compared other solutions such as the PHT strategy and its optimized version which includes a query cache.

Semantic cache replacement strategy for XML algebra-based query optimization

Aiming at the fact that traditional cache replacement strategy lacks pertinence to the semantic cache in the process of extensible markup language (XML) algebra query, a replacement strategy based on the semantic cache contribution value is proposed. First, pattern matching rules for XML algebra query and semantic caches are given. Second, the method of calculating the semantic cache contribution value is proposed. In XML documents with four different sizes, the experimental results of time efficiency show that this strategy supports environment of the XML algebra query and it has better time efficiency than both least frequency used (LFU) and least recently used (LRU).

A Dependable Computing in Database for Managing Mass Connection Systems

Most operations in database (DB) is based on the deferred update replication technique, especially in distributed environments. In deferred update replication, transactio ns are executed on a single host and broadcast to other hosts at commit time. Upon delivering a transaction, each host confirms it to ensure a globally serializable execution. The mechanism is subject to CAP theorem in order to ensure data consistency and availability. This paper proposes a dependable cache query mechanism in distributed relational database (DRDB). The key insight of the cache query mechanism provides high performance, strong consistency and availability for mass connection systems. In addition to presenting the cache query mechanism, we elaborate its implementation, and provide an experiment and analysis of its performance.

A Dependable Computing in Database for Managing Mass Connection Systems

Most operations in database (DB) is based on the deferred update replication technique, especially in distributed environments. In deferred update replication, transactio ns are executed on a single host and broadcast to other hosts at commit time. Upon delivering a transaction, each host confirms it to ensure a globally serializable execution. The mechanism is subject to CAP theorem in order to ensure data consistency and availability. This paper proposes a dependable cache query mechanism in distributed relational database (DRDB). The key insight of the cache query mechanism provides high performance, strong consistency and availability for mass connection systems. In addition to presenting the cache query mechanism, we elaborate its implementation, and provide an experiment and analysis of its performance.

Caching Support for Skyline Query Processing with Partially Ordered Domains

Existing methods have addressed the issue of handling each individual skyline query performed on data sets with partially ordered domains. However, it is still very challenging to process such queries for on-line applications with low response time. In this paper, we introduce a cache-based framework, called CSS, for further reducing the query processing time to support high-responsive applications. Skyline queries that were previously processed with user preferences similar to those of the current query contribute useful candidate result points. Hence, the answered queries are cached with both their results and user preferences such that the query processor can rapidly retrieve the result for a new query only from the result sets of selected queries with compatible user preferences. We introduce a similarity measure that establishes the level of similarity between the user preferences of a new query and a cached query; hence the system can start with the most similar candidates. Furthermore, if a new query is only partially answerable from the cache, then the query processor utilizes the partial result sets and performs less expensive constraint skyline queries guided by violated preferences. Furthermore, we introduce two access methods for cached queries indexed by their user preferences to only access a set of relevant cached queries for similarity measures. Extensive experiments are presented to demonstrate the performance and utility of our novel approach.

Answering keyword queries through cached subqueries in best match retrieval models

Caching is one of the techniques that Information Retrieval Systems (IRS) and Web Search Engines (WSEs) use to reduce processing costs and attain faster response times. In this paper we introduce Top-K SCRC (Set Cover Results Cache), a novel technique for results caching which aims at maximizing the utilization of cache. Identical queries are treated as in plain results caching (i.e. their evaluation does not require accessing the index), while combinations of cached sub-queries are exploited as in posting lists caching, however the exploited subqueries are not necessarily single-word queries. The problem of finding the right set of cached subqueries to answer an incoming query, is actually the Exact Set Cover problem. This technique can be applied in any best match retrieval model that is based on a decomposable scoring function, and we show that several best-match retrieval models (i.e VSM, Okapi BM25 and hybrid retrieval models) rely on such scoring functions. To increase the capacity (in queries) of the cache only the top-K results of each cached query are stored and we introduce metrics for measuring the accuracy of the composed top-K answer. By analyzing queries submitted to real-world WSEs, we verified that there is a significant proportion of queries whose terms is the result of a union of the terms of other queries. The comparative evaluation over traces of real query sets showed that the Top-K SCRC is on the average two times faster than a plain Top-K RC for the same cache size.

Query Optimization in OODBMS: Identifying Subquery for Query Management

This paper is based on relatively newer approach for query optimization in object databases, which uses query decomposition and cached query results to improve execution a query. Issues that are focused here is fast retrieval and high reuse of cached queries, Decompose Query into Sub query, Decomposition of complex queries into smaller for fast retrieval of result. Here we try to address another open area of query caching like handling wider queries. By using some parts of cached results helpful for answering other queries (wider Queries) and combining many cached queries while producing the result. Multiple experiments were performed to prove the productivity of this newer way of optimizing a query. The limitation of this technique is that it’s useful especially in scenarios where data manipulation rate is very low as compared to data retrieval rate.

Applications of Soundexing Retrieval and Query Caching in an Open-Source-Software Based Library Catalogue System

Applications of Soundexing Retrieval and Query Caching in an Open-Source-Software Based Library Catalogue System In some small organizations with limited resources, based on authors’ survey, the library facilities could be decade old. Current marketing prices of library software, including upkeep costs, are prohibitively high. Improving and updating these library information systems by employing open-source-software might provide a better solution. In this paper, the authors present the design of an open source software based library catalogue system that takes multi-tier client/server architecture. Furthermore, techniques of soundexing retrieval and query caching are applied to enhance the system performance with respect to error tolerance, searching speed and scalability. With the support of an appropriately designed soundex algorithm, the catalogue system can largely increase its recall while not compromise the searching precision. The introduced query cache, on the other hand, can increase significantly the system response speed and thus the scalability. The performance of the developed prototype system is verified by simulations using real library data.