Query Response Research Articles

Efficient top/bottom-k fraction estimation in spatial databases using bounded main memory

Spatial databases store objects with their locations and certain types of attached items. A variety of modern applications have been developed by leveraging the utilization of locations and items in spatial objects, such as searching points of interest, hot topics, or users' attitude in specified spatial regions. In many scenarios, the high and low-frequency items in a spatial region are worth noticing, considering they represent the majority's interest or eccentric users' opinion. However, existing works have yet to identify such items in an interactive manner, despite the significance of the endeavor in decision-making systems. This study recognizes a novel type of analytical query, called top/bottom-k fraction query, to discover such items in spatial databases. To achieve fast query response, we propose a multilayered data summary that is spread out across the main memory and external memory. A memory-based estimation method for top/bottom-k fraction queries is proposed. To maximize the use of the main memory space, we design a data summary tuning method to dynamically allocate memory space among different spatial partitions. The proposed approach is evaluated with real-life datasets and synthetic datasets in terms of estimation accuracy. Evaluation results demonstrate the effectiveness of the proposed data summary and corresponding estimation and tuning algorithms.

Adaptive similarity search for the retrieval of rare events from large time series databases

Improving the recall of information retrieval systems for similarity search in time series databases is of great practical importance. In the manufacturing domain, these systems are used to query large databases of manufacturing process data that contain terabytes of time series data from millions of parts. This allows domain experts to identify parts that exhibit specific process faults. In practice, the search often amounts to an iterative query–response cycle in which users define new queries (time series patterns) based on results of previous queries. This is a well-documented phenomenon in information retrieval and not unique to the manufacturing domain. Indexing manufacturing databases to speed up the exploratory search is often not feasible as it may result in an unacceptable reduction in recall. In this paper, we present a novel adaptive search algorithm that refines the query based on relevance feedback provided by the user. Additionally, we propose a mechanism that allows the algorithm to self-adapt to new patterns without requiring any user input. As the search progresses, the algorithm constructs a library of time series patterns that are used to accurately find objects of the target class. Experimental validation of the algorithm on real-world manufacturing data shows, that the recall for the retrieval of fault patterns is considerably higher than that of other state-of-the-art adaptive search algorithms. Additionally, its application to publicly available benchmark data sets shows, that these results are transferable to other domains.

Differential Privacy via a Truncated and Normalized Laplace Mechanism

When querying databases containing sensitive information, the privacy of individuals stored in the database has to be guaranteed. Such guarantees are provided by differentially private mechanisms which add controlled noise to the query responses. However, most such mechanisms do not take into consideration the valid range of the query being posed. Thus, noisy responses that fall outside of this range may potentially be produced. To rectify this and therefore improve the utility of the mechanism, the commonly-used Laplace distribution can be truncated to the valid range of the query and then normalized. However, such a data-dependent operation of normalization leaks additional information about the true query response, thereby violating the differential privacy guarantee. Here, we propose a new method which preserves the differential privacy guarantee through a careful determination of an appropriate scaling parameter for the Laplace distribution. We adapt the privacy guarantee in the context of the Laplace distribution to account for data-dependent normalization factors and study this guarantee for different classes of range constraint configurations. We provide derivations of the optimal scaling parameter (i.e., the minimal value that preserves differential privacy) for each class or provide an approximation thereof. As a result of this work, one can use the Laplace distribution to answer queries in a range-adherent and differentially private manner. To demonstrate the benefits of our proposed method of normalization, we present an experimental comparison against other range-adherent mechanisms. We show that our proposed approach is able to provide improved utility over the alternative mechanisms.

Implementation of Database Distributed Sharding Horizontal Partition in MySQL. Case Study of Application of Food Serving On Kemkes

In today's digital era, database systems are becoming a mandatory thing that is used in every industry field to improve the performance of the industry, over time with the increasing need for databases, single databases and single servers are considered less capable in handling data that continues to increase in number, so it often leads to a decline in performance. Therefore came the idea of centralized data storage that can be modified to accommodate the availability, scalability, reliability, and management of data. One way that can be done is to apply Distribute Database using Sharding Horizontal Partition technique and experimental methods using mariadb and spider engine. The test was carried out on the database of food processing sites (TPP) in the TPP application at the ministry of health. The test results prove that although in some journals about Sharding, more sharding is implemented on NoSQL DBMS such as MongoDB, influx dB. In this study it can be proven that sharding can also be done in relational DBMS such as MariaDB and MySQL, data can be distribute and load process too, test result successful although the performance of time query response is still better single database than distribute database.

SPHT: A scalable and high‐performance hashing scheme for persistent memory

AbstractThe evolution of persistent memory (PM) has significantly affected the design of today's indexing structures. Hashing‐based structures are widely used in storage systems to achieve fast query responses. Recently, several concurrent and failure‐atomic hashing schemes for PM have been proposed to improve the scalability. However, these works still suffer from limited scalability, especially under write‐intensive workloads or at a high number of threads. Our empirical study concludes three issues harm the scalability of PM hashing schemes: the NUMA effects, the resizing operations, and the inter‐thread interference overhead in PM. Based on the above scalability issues, we present SPHT, a scalable and persistent hashing scheme for hybrid DRAM‐PM memory. To eliminate the NUMA effects, SPHT maintains a hash subtable for every NUMA node and stores the key‐value items in the designated nodes. By doing so, all operations can be executed in the local memory without cross‐node communication. The hash subtable consists of two components: the search layer in DRAM for fast accesses and the data layer in PM for efficient persistence. The data layer is organized in a log‐structured way and its log chunks own separate PM space, thus avoiding the resizing operations in PM. Meanwhile, the compacted log structure also supports batching multiple small items, which effectively reduces the persistence overhead. Furthermore, to maximize concurrency, we assign threads to different partitions in the data layer to reduce the inter‐thread interference overhead. On Intel Optane DCPMM, our evaluations show that SPHT scales well and achieves up to 2.7 higher performance than state‐of‐the‐art PM hashing schemes under YCSB workloads.

A foundation for spatio-textual-temporal cube analytics

Large amounts of spatial, textual, and temporal (STT) data are being produced daily. This is data containing an unstructured component (text), a spatial component (geographic position), and a time component (timestamp). Therefore, there is a need for a powerful and general way of analyzing STT data together. In this paper, we define and formalize the Spatio-Textual-Temporal Cube (STTCube) structure to enable combined effective and efficient analytical queries over STT data. Our novel data model over STT objects enables novel joint and integrated STT insights that are hard to obtain using existing methods. Furthermore, our proposed STTCube Incremental Maintenance (IMstt) method maintains the already constructed STTCube efficiently when new data arrives. Moreover, we introduce the new concept of STT measures with associated novel STT-OLAP operators. To allow for efficient large-scale analytics, we present a pre-aggregation framework for exact and approximate computation of STT measures. Our comprehensive experimental evaluation on a real-world Twitter dataset confirms that our proposed methods reduce query response time by 1–5 orders of magnitude compared to the No Materialization baseline and decrease storage cost between 97% and 99.9% compared to the Full Materialization baseline while adding only a negligible overhead in the STTCube construction time. Moreover, approximate computation achieves an accuracy between 90% and 100% while reducing query response time by 3–5 orders of magnitude compared to No Materialization and IMstt achieves an order of magnitude improvement in maintenance time compared to the baseline maintenance method.

The unfolding and activation of STIM1 in store-operated calcium signal generation

• Single molecule FRET studies reveal details of the folding of STIM1 in its resting state and unfolding activated by Ca 2+ store-depletion. • STIM1 is folded with SOAR inverted and held between the CC1α helices in a domain-swapping configuration close to the ER membrane. • Store-depletion causes the STIM1 TM-helices to bind together, squeezing SOAR from between the CC1α1 helices, allowing it to flip up and activate Orai channels SEE ABSTRACT in Query Response

Optimized Query Algorithms for Top- K Group Skyline

Skyline query is a typical multiobjective query and optimization problem, which aims to find out the information that all users may be interested in a multidimensional data set. Multiobjective optimization has been applied in many scientific fields, including engineering, economy, and logistics. It is necessary to make the optimal decision when two or more conflicting objectives are weighed. For example, maximize the service area without changing the number of express points, and in the existing business district distribution, find out the area or target point set whose target attribute is most in line with the user’s interest. Group Skyline is a further extension of the traditional definition of Skyline. It considers not only a single point but a group of points composed of multiple points. These point groups should not be dominated by other point groups. For example, in the previous example of business district selection, a single target point in line with the user’s interest is not the focus of the research, but the overall optimality of all points in the whole target area is the final result that the user wants. This paper focuses on how to efficiently solve top- k group Skyline query problem. Firstly, based on the characteristics that the low levels of Skyline dominate the high level points, a group Skyline ranking strategy and the corresponding SLGS algorithm on Skyline layer are proposed according to the number of Skyline layer and vertices in the layer. Secondly, a group Skyline ranking strategy based on vertex coverage is proposed, and corresponding VCGS algorithm and optimized algorithm VCGS+ are proposed. Finally, experiments verify the effectiveness of this method from two aspects: query response time and the quality of returned results.

A bespoke target selection tool to guide biomarker discovery in tubo-ovarian cancer

IntroductionCancers presenting at advanced stages inherently have poor prognosis. High grade serous carcinoma (HGSC) is the most common and aggressive form of tubo-ovarian cancer. Clinical tests to accurately diagnose and monitor this condition are lacking. Hence, development of disease-specific tests are urgently required. MethodsThe molecular profile of HGSC during disease progression was investigated in a unique patient cohort. A bespoke data browser was developed to analyse gene expression and DNA methylation datasets for biomarker discovery. The Ovarian Cancer Data Browser (OCDB) is built in C# with a.NET framework using an integrated development environment of Microsoft Visual Studio and fast access files (.faf). The graphical user interface is easy to navigate between four analytical modes (gene expression; methylation; combined gene expression and methylation data; methylation clusters), with a rapid query response time. A user should first define a disease progression trend for prioritising results. Single or multiomics data are then mined to identify probes, genes and methylation clusters that exhibit the desired trend. A unique scoring system based on the percentage change in expression/methylation between disease stages is used. Results are filtered and ranked using weighting and penalties. ResultsThe OCDB’s utility for biomarker discovery is demonstrated with the identified target OSR2. Trends in OSR2 repression and hypermethylation with HGSC disease progression were confirmed in the browser samples and an independent cohort using bioassays. The OSR2 methylation biomarker could discriminate HGSC with high specificity (95%) and sensitivity (93.18%). ConclusionsThe OCDB has been refined and validated to be an integral part of a unique biomarker discovery pipeline. It may also be used independently to aid identification of novel targets. It carries the potential to identify further biomarker assays that can reduce type I and II errors within clinical diagnostics.

Comparative Analysis of OSPFv3/IS-IS and RIPng/IS-IS Mixed Protocols for Real-Time Applications in IPv6 Communication Networks

In network designs, the decision made when implementing dynamic routing protocols is very paramount to the speed of the network. To make the best choice of protocol to deploy, several decisions has to be considered. Usually, these decisions are made based on the performance of the routing protocol with respect to some quantitative parameters. The protocol that performs better than other protocols involved in a research is selected for routing purposes. In this research paper, performance comparison of two mixed protocols namely OSPFv3/IS-IS and RIPng/IS-IS in IPv6 network has been made. Their performances have been measured and comparison made by simulation using Riverbed Modeller Academic Edition. The objective of this paper is mainly to determine which of the mixed protocols will be more suitable to route traffic in IPv6 network. The main motivation for this paper is to find out if the difference in the routing algorithms of RIPng and IS-IS will offset and produce a better performance than a combination of two routing protocols of the same routing algorithm (thus OSPFv3 and IS-IS). To achieve this paper’s objective, the simulation was divided into two scenarios. The first scenario was an OSPFv3/IS-IS configured IPv6 network topology. The second scenario is a copy of the first scenario but configured with RIPng/IS-IS. The two scenarios were simulated and the effect of using each of the scenarios to separately route the selected applications was measured and recorded. The performance comparison of the mixed protocols was based on the following quantitative parameters: database query response time, database query traffics received, email upload/download response time, ftp upload/download response time, ftp traffic received, http page response time, remote login response timeandIPv6 traffics dropped. The results obtained from the simulation indicated that RIPng/IS-IS scenario performed better in email download/upload response time, remote login response time, IPv6 traffics dropped and remote login response time while the mixture of OSPFv3/IS-IS performed better in database query response time, database query traffics received, ftp download/upload response time, ftp traffic received and http page response time. Hence OSPFv3/IS-IS is the better option when the choice is between RIPng/IS-IS and OSPFv3/IS-IS for most of the quantitative parameters involved in this paper. This is because the combination of RIPng and IS-IS took a longer time to converge, affecting the speed on the network scenario. The time the RIPng/IS-IS combination took to access most of the application servers is slower than that of OSPFv3/IS-IS network scenario. On the basis of database query and ftp traffics received, the simulation results showed that network configured with OSPFv3/IS-IS performs better than RIPng/IS-IS. This is because the OSPFv3/IS-IS received the highest database and ftp traffics. The mixture of OSPFv3/IS-IS sent and received more application packets because it had very high throughput values which had an effect on the total quantity of application traffics received. Although the OSPFv3/IS-IS network scenario recorded the highest database and ftp traffics, this could not affect its speed to become lower than the RIPng/IS-IS scenario.

Adaptive replication strategy based on popular content in cloud computing

The cloud is an infrastructure that provides decentralized on-demand services. It allows consumers to pay only for the services they use. The consumer is the important entity in the cloud. The violation of the SLA contract between the consumer and the provider often leads to consequences because the service provider has to pay penalties. Data replication is emerging as an ideal solution to meet the new challenges of the cloud. This paper proposes a new replication strategy based on the popularity of data. This strategy adaptively selects the files to be replicated to improve the overall availability of data in the system, minimize query response time, and achieve the required quality of service. In addition, it dynamically determines the number of replicas to add and the best locations to store them. Experimental results show the effectiveness of the proposed strategy.

Optimized execution method for queries with materialized views: Design and implementation

A query application for On-Line Analytical Processing (OLAP) examines various kinds of data stored in a Data Warehouse (DW). There have been no systematic studies that look at the impact of query optimizations on performance and energy consumption in relational and NoSQL databases. Indeed, due to a lack of precise power calculation techniques in various databases and queries, the energy activity of several basic database operations is mostly unknown, as are the queries themselves, which are very complicated, extensive, and exploratory. As a result of the rapidly growing size of the DW system, query response times are regularly increasing. To improve decision-making performance, the response time of such queries should be as short as possible. To resolve these issues, multiple materialized views from individual database tables have been collected, and queries have been handled. Similarly, due to overall maintenance and storage expenses, as well as the selection of an optimal view set to increase the data storage facility’s efficacy, materializing all conceivable views is not viable. Thus, to overcome these issues, this paper proposed the method of energy-aware query optimization and processing, on materialized views using enhanced simulated annealing (EAQO-ESA). This work was carried out in four stages. First, a Simulated Annealing (SA) based meta-heuristic approach was used to pre-process the query and optimize the scheduling performance. Second, the optimal sets of views were materialized, resulting in enhanced query response efficiency. Third, the authors assessed the performance of the query execution time and computational complexity with and without optimization. Finally, based on processing time, efficiency, and computing cost, the system’s performance was validated and compared to the traditional technique.

SDN-based dynamic resource management and scheduling for cognitive industrial IoT

PurposeIn recent years, it is imperative to establish the structure of manufacturing industry in the context of smart factory. Due to rising demand for exchange of information with various devices, and huge number of sensor nodes, the industrial wireless networks (IWNs) face network congestion and inefficient task scheduling. For this purpose, software-defined network (SDN) is the emerging technology for IWNs, which is integrated into cognitive industrial Internet of things for dynamic task scheduling in the context of industry 4.0.Design/methodology/approachIn this paper, the authors present SDN based dynamic resource management and scheduling (DRMS) for effective devising of the resource utilization, scheduling, and hence successful transmission in a congested medium. Moreover, the earliest deadline first (EDF) algorithm is introduced in authors’ proposed work for the following criteria’s to reduce the congestion in the network and to optimize the packet loss.FindingsThe result shows that the proposed work improves the success ratio versus resource usage probability and number of nodes versus successful joint ratio. At last, the proposed method outperforms the existing myopic algorithms in terms of query response time, energy consumption and success ratio (packet delivery) versus number of increasing nodes, respectively.Originality/valueThe authors proposed a priority based scheduling between the devices and it is done by the EDF approach. Therefore, the proposed work reduces the network delay time and minimizes the overall energy efficiency.

XML2HBase: Storing and querying large collections of XML documents using a NoSQL database system

Many big data applications such as smart transportation, healthcare, and e-commerce need to store and query large collections of small XML documents, which has become a fundamental problem. However, existing solutions are inadequate to deliver satisfactory query performance in such circumstances. In this paper, we propose a framework named XML2HBase to address this problem using HBase, a widely deployed NoSQL database. Within this framework, we design a novel encoding scheme called Pathed-Dewey Order and a two-layer mapping method to store XML documents in HBase tables. XML queries, which are represented as XPath expressions, are evaluated through their translation into queries over HBase tables. Based on an in-depth analysis of the characteristics of the proposed approach, we design and integrate four optimization strategies to reduce storage space and query response time. Extensive experiments on two well-known XML benchmarks demonstrate the superior performance of XML2HBase over three state-of-the-art methods.

Cracking in-memory database index: A case study for Adaptive Radix Tree index

Indexes provide a method to access data in databases quickly. It can improve the response speed of subsequent queries by building a complete index in advance. However, it also leads to a huge overhead of the continuous updating during creating the index. An in-memory database usually has a higher query processing performance than disk databases and is more suitable for real-time query processing. Therefore, there is an urgent need to reduce the index creation and update cost for in-memory databases. Database cracking technology is currently recognized as an effective method to reduce the index initialization time. However, conventional cracking algorithms are focused on simple column data structure rather than those complex index structures for in-memory databases. In order to show the feasibility of in-memory database index cracking and promote to future more extensive research, this paper conducted a case study on the Adaptive Radix Tree (ART), a popular tree index structure of in-memory databases. On the basis of carefully examining the ART index construction overhead, an algorithm using auxiliary data structures to crack the ART index is proposed. This makes it possible to build up an ART index step by step with incessant queries, and hence avoids the poor instant availability of a complete index which is constructed once and for all, but is time consuming. Furthermore, updating a cracking ART index is considered as well. Extensive experiments show that the average initialization time of the ART cracker index is reduced by 75%, and the query response time gradually approaches the original ART algorithm with the coming queries.

Assessing the Performance Analysis of OSPFV3 and EIGRP in Applications in IPV6 Analysis for Articles Published in Scopus between 2016 and 2021

The Analysis of common conceptual frameworks associated with Performance analysis of OSPFV3 and EIGRP in applications in IPV6 for analysis of articles published in Scopus between 2016 and 2021 by applying the Corresponding method analysis. The number of times an article is downloaded is also being considered as a measurement instrument or method of analysis. The Corresponding analysis method has analysis 117 articles from 2016 to 2021. All the articles are based on performance analysis of OSPFV3 AND IPV6.IPv6 has gained legitimacy and inevitability as a result of the internet's expansion, which has resulted in IPv4 address space exhaustion. An internet next-generation protocol that will replace eventually IPv4 is IPv6. Using Riverbed Modeler Academic Edition, 2state link protocols’ performance for IPv6, IS–IS and OSPFv3 was compared and tested for the greatest commonly utilized applications enterprise for example remote login, database query, file transfer, web surfing, and email. The major characteristics used to assess performance include IPv6 packets dropped, network convergence time, link utilization, throughput, remote login response time, file upload/download response times, http page response times, email, and database query response time,. The primary goal of this dissertation is to compare, simulate, and assess both routing protocols’ performance in order to decide which one is best for routing IPv6 network traffic. Based on the parameters utilized, the protocol that performed better than the others would be suggested for routing network traffic in IPv6. The study was separated into two scenarios to achieve this goal: the IS–IS and OSPFv3 scenario. After the simulation for the IS–IS scenario was completed, the data from both scenarios were compared and examined using the provided parameters to see which protocol worked better. Based on the majority of the simulation parameters employed, the simulation results showed that OSPFv3 was performed as compared toIS–IS.

Efficient Indexing of Top-k Entities in Systems of Engagement with Extensions for Geo-tagged Entities

Next-generation enterprise management systems are beginning to be developed based on the Systems of Engagement (SOE) model. We visualize an SOE as a set of entities. Each entity is modeled by a single parent document with dynamic embedded links (i.e., child documents) that contain multi-modal information about the entity from various networks. Since entities in an SOE are generally queried using keywords, our goal is to efficiently retrieve the top-k entities related to a given keyword-based query by considering the relevance scores of both their parent and child documents. Furthermore, we extend the afore-mentioned problem to incorporate the case where the entities are geo-tagged. The main contributions of this work are three-fold. First, it proposes an efficient bitmap-based approach for quickly identifying the candidate set of entities, whose parent documents contain all queried keywords. A variant of this approach is also proposed to reduce memory consumption by exploiting skews in keyword popularity. Second, it proposes the two-tier HI-tree index, which uses both hashing and inverted indexes, for efficient document relevance score lookups. Third, it proposes an R-tree-based approach to extend the afore-mentioned approaches for the case where the entities are geo-tagged. Fourth, it performs comprehensive experiments with both real and synthetic datasets to demonstrate that our proposed schemes are indeed effective in providing good top-k result recall performance within acceptable query response times.

A secure and efficient range query method for two‐layer wireless sensor networks

Traditional range query methods of work still have shortcomings in node energy consumption and privacy security, so a two-layer secure and efficient range query method for wireless sensor networks is proposed. In the data storage stage, the sensing node obtains the data ciphertext and timestamp by the Advanced Encryption Standard encryption algorithm, receives the new encryption constraint chain by the reverse 0–1 encoding method and Hash-based Message Authentication Code encryption algorithm, and sends the chain to the storage node. In the query response phase, the storage node responds to the request of the base station and sends the data that meet the query requirements. After receiving the data, the base station verifies the consistency with the new encryption constraint chain and timestamp. During the experiment, the energy consumption is analysed from three aspects: the number of data collected in the period, the data length of the sensing node and the partition factor of the encryption constraint chain. The results show that this method has low energy consumption and can maintain the consistency of data.

Computational resources to define alleles and altered regulatory motifs at genomically edited candidate response elements.

Unequivocal functional assessment of candidate genomic regulatory regions, such as transcriptional response elements, requires genetic alteration at their native chromosomal loci. Targeted DNA cleavage by Cas9 or other programmable nucleases enables analysis at virtually any genomic region, and diverse alleles generated by editing can be defined by deep sequencing for functional analysis. Interpretation of disrupted response elements, however, presents a special challenge, as these regions typically comprise clustered DNA binding motifs for multiple transcriptional regulatory factors (TFs); DNA sequence differences, natural or engineered, that affect binding by one TF can confer loss or gain of binding sites for other TFs. To address these and other analytical complexities, we created three computational tools that together integrate, in a single experiment, allele definition and TF binding motif evaluation for up to 9216 clones isolated, sequenced and propagated from Cas9-treated cell populations. We demonstrate 1) the capacity to functionally assess edited TF binding sites to query response element function, and 2) the efficacy and utility of these tools, by analyzing cell populations targeted by Cas9 for disruption of example glucocorticoid receptor (GR) binding motifs near FKBP5, a GR-regulated gene in the human adenocarcinoma cell line A549.

Temporal Geo-Social Personalized Keyword Search Over Streaming Data

The unprecedented rise of social media platforms, combined with location-aware technologies, has led to continuously producing a significant amount of geo-social data that flows as a user-generated data stream. This data has been exploited in several important use cases in various application domains. This article supports geo-social personalized queries in streaming data environments. We define temporal geo-social queries that provide users with real-time personalized answers based on their social graph. The new queries allow incorporating keyword search to get personalized results that are relevant to certain topics. To efficiently support these queries, we propose an indexing framework that provides lightweight and effective real-time indexing to digest geo-social data in real time. The framework distinguishes highly dynamic data from relatively stable data and uses appropriate data structures and a storage tier for each. Based on this framework, we propose a novel geo-social index and adopt two baseline indexes to support the addressed queries. The query processor then employs different types of pruning to efficiently access the index content and provide a real-time query response. The extensive experimental evaluation based on real datasets has shown the superiority of our proposed techniques to index real-time data and provide low-latency queries compared to existing competitors.