Query Response Time Research Articles

Database Optimization Techniques with Logic Execution Optimization on Microservices Architecture

Microservices architecture, a distributed framework architecture that allows changes to one module without interfering with other modules. The implementation of this architecture has its own challenges. The get-list-attachment API running on this architecture takes an average of 12.5 seconds to serve data. This needs to be considered because business processes require shorter access times to support decision making. The research objective is to obtain query response time efficiency for accounting applications. To achieve this, the research uses database optimization techniques with logic execution optimization microservices architecture. This study obtained the source of information from the Accounting Harmony Accounting Module, which has an API (get-list-attachment) with data sourced from Service Accounting (581253 records) and Service Users (2182 records). Based on a series of tests carried out, several services need to be added with APIs to improve the microservices architecture to accept bulk parameters that generate a list of objects so that data presentation is more optimal. After doing a series of engineering on microservices architecture and indexing application, query response time performance increased by 49.22% for Service Accounting module.

LAQy: Efficient and Reusable Query Approximations via Lazy Sampling

Modern analytical engines rely on Approximate Query Processing (AQP) to provide faster response times than the hardware allows for exact query answering. However, existing AQP methods impose steep performance penalties as workload unpredictability increases. Specifically, offline AQP relies on predictable workloads to create samples that match the queries in a priori to query execution, reducing query response times when queries match the expected workload. As soon as workload predictability diminishes, existing online AQP methods create query-specific samples with little reuse across queries, producing significantly smaller gains in response times. As a result, existing approaches cannot fully exploit the benefits of sampling under increased unpredictability. We analyze sample creation and propose LAQy, a framework for building, expanding, and merging samples to adapt to the changes in workload predicates. We show the main parameters that affect the sample creation time and propose lazy sampling to overcome the unpredictability issues that cause fast-but-specialized samples to be query-specific. We evaluate LAQy by implementing it in an in-memory code-generation-based scale-up analytical engine to show the adaptivity and practicality of our framework in a modern system. LAQy speeds up online sampling processing as a function of sample reuse ranging from practically zero to full online sampling time and from 2.5x to 19.3x in a simulated exploratory workload.

A Dynamic Grid Index for CkNN Queries on Large-Scale Road Networks with Moving Objects

As the Internet of Things devices are deployed on a large scale, location-based services are being increasingly utilized. Among these services, kNN (k-nearest neighbor) queries based on road network constraints have gained importance. This study focuses on the CkNN (continuous k-nearest neighbor) queries for non-uniformly distributed moving objects with large-scale dynamic road network constraints, where CkNN objects are continuously and periodically queried based on their motion evolution. The present CkNN high-concurrency query under the constraints of a super-large road network faces problems, such as high computational cost and low query efficiency. The aim of this study is to ensure high concurrency nearest neighbor query requests while shortening the query response time and reducing global computation costs. To address this issue, we propose the DVTG-Index (Dynamic V-Tree Double-Layer Grid Index), which intelligently adjusts the index granularity by continuously merging and splitting subgraphs as the objects move, thereby filtering unnecessary vertices. Based on DVTG-Index, we further propose the DVTG-CkNN algorithm to calculate the initial kNN query and utilize the existing results to speed up the CkNN query. Finally, extensive experiments on real road networks confirm the superior performance of our proposed method, which has significant practical applications in large-scale dynamic road network constraints with non-uniformly distributed moving objects.

Decision-aided evaluation of paths for routing on multi-attribute computer network consisting of encoded paths under uncertainty

The network users need to evaluate the alternative transmission pathways to avoid some routing problems such as possible network attacks, data traffic, etc. in computer networks. In order to provide a mathematical solution to this situation, in this paper the computer network system is considered as a graph, and a random data structure denoted by Bloom filter is used to forward data between computers in the network. The Bloom filter has been used for encoding the routes minimises the query response time, although it has a significant risk of producing errors indicated by false positives (FP). The study aims to assess all paths between two distinct computers on a multi-attribute computer network considering risk and FP values regarding paths. These considerations are inherently vagueness. That being the case, a novel two-stage research methodology is presented to determine the desired pathways during message transmission on a multi-attribute computer network under uncertainty. At this point, the values of the possible risks and the probability of FP of the transmission edges are defined by employing triangular fuzzy numbers to reach the theoretical solutions. The novelty of the study is proposing a research methodology based on fuzzy Multi-Criteria Decision Making (MCDM) techniques to offer alternative routes during data transmission on any multi-attribute computer network under uncertainty for the first time. Further, a sensitivity analysis is implemented. In all computational analyses, the most appropriate and reliable transmission pathways are listed, and some managerial insights and main findings are provided for the network users.

An effective ontology-based query response model for risk assessment in urban flood disaster management

The drastic advancements in the field of Information Technology make it possible to analyze, manage and handle large-scale environment data and spatial information acquired from diverse sources. Nevertheless, this process is a more challenging task where the data accessibility has been performed in an unstructured, varied, and incomplete manner. The appropriate extraction of information from diverse data sources is crucial for evaluating natural disaster management. Therefore, an effective framework is required to acquire essential information in a structured and accessible manner. This research concentrates on modeling an efficient ontology-based evaluation framework to facilitate the queries based on the flood disaster location. It offers a reasoning framework with spatial and feature patterns to respond to the generated query. To be specific, the data is acquired from the urban flood disaster environmental condition to perform data analysis hierarchically and semantically. Finally, data evaluation can be accomplished by data visualization and correlation patterns to respond to higher-level queries. The proposed ontology-based evaluation framework has been simulated using the MATLAB environment. The result exposes that the proposed framework obtains superior significance over the existing frameworks with a lesser average query response time of 7 seconds.

Efficient Processing of k-Hop Reachability Queries on Directed Graphs

Given a directed graph, a k-hop reachability query, u→?kv, is used to check for the existence of a directed path from u to v that has a length of at most k. Addressing k-hop reachability queries is a fundamental task in graph theory and has been extensively investigated. However, existing algorithms can be inefficient when answering queries because they require costly graph traversal operations. To improve query performance, we propose an approach based on a vertex cover. We construct an index that covers all reachability information using a small set of vertices from the input graph. This allows us to answer k-hop reachability queries without performing graph traversal. We propose a linear-time algorithm to quickly compute a vertex cover, S, which we use to develop a novel labeling scheme and two algorithms for efficient query answering. The experimental results demonstrate that our approach significantly outperforms the existing approaches in terms of query response time.

BTLA-LSDG: Blockchain-Based Triune Layered Architecture for Authenticated Subgraph Query Search in Large-Scale Dynamic Graphs

Over the past era, subgraph mining from a large collection of a graph database is a crucial problem. our proposed work introduces a Blockchain-based Triune Layered Architecture for authenticated subgraph query search in large-scale dynamic graphs. The two-fold process is handled in BTLA-LSDG: graph indexing and authenticated query search (query processing), which are implemented in triune layers (Data Generation Layer, Data Storage Layer, and Service Layer). Initially, data owners are authenticated to the blockchain using the Four-Q-Curve algorithm. The graph index is constructed by data owners and the merged graph index is constructed by service providers. Based on the uploaded graph index, the hash index is constructed using SHA-3. On the other hand, the data user submits a query with authentication. For every authenticated query, the four-fold process is handled. Firstly, Multi-Constraint-based Belief Entropy function is used for feature set computations for a given query. Then dual similarity-based MapReduce helps in mapping and reducing the relevant subgraphs with the use of optimal feature sets. Thirdly, a Recurrent Neural Network (RNN) is used for subgraph isomorphic testing. Finally, the graph index refinement process is undertaken to improve the query results. This experiment is implemented using a Hadoop environment and the results show better efficiency in terms of Scalability, Security and Storage. Furthermore, it is tested with Precision, Recall, F-measure, Accuracy, Error Rate, Query Response Time and Positive Results.

Arriving at the Results by Comparing with Traditional Approach to My Approach in Deriving Function Points for ETL Operations

It can be hard to guess how much data will need to be put into the data warehouse when the whole history of the transaction system is moved there. This is especially true when the transfer process could take weeks or even months. The ETL system's parts must be broken down into its three independent stages, nevertheless, when estimating a big starting load. Data extraction from source systems, Creating the dimensional model from data, Loading the data warehouse and timing estimates for the extraction process. Surprisingly, data extraction from the source system may take up the majority of the ETL procedure. Online transaction processing (OLTP) systems are simply not built to return those massive data sets from the data warehouse's historic load, which extracts a tremendous quantity of data in a single query. However, the daily incremental loads and the breath-of-life historic database loads are very different. In any case, fact-table filling requires data to be pulled in a different way than what transaction systems are able to do. ETL extraction procedures frequently call for time-consuming techniques like views, cursors, stored procedures, and correlated subqueries. It is essential to anticipate how long an extract will take to begin before it does. Calculating the extract time estimate is challenging. Due to the hardware mismatch between the test and production servers, estimates based on the execution of the ETL operations in the test environment may be greatly distorted. Sometimes working on certain projects where an extract task would run continuously and until it eventually failed, at which point it would be restarted and run once more until it failed. Without producing anything, days or even weeks passed. One must divide the extract process into two simpler steps in order to overcome the challenges of working with large amounts of data. Response time for queries. the interval between when the query is conducted and when the data starts to be returned. It is pertinent that effort arrival for ETL Operations for Data Marts and DWH projects in terms of Function Points which is a scientific way is essential. In the last paper, I have talked about general System Characteristics to arrive at Value Adjustment Factor. In this paper, I came up with results. I compared my findings with the conventional FPA on industrial projects in order to evaluate the Function Point Analysis’s suitability for Data Mart projects. I outline the strategy, implementation, and outcomes analysis of this validation in this section.

Improving NoSQL Spatial-Query Processing with Server-Side In-Memory R*-Tree Indexes for Spatial Vector Data

Geospatial databases are basic tools to collect, index, and manage georeferenced data indicators in sustainability research for efficient, long-term analysis. NoSQL databases are increasingly applied to manage the ever-growing massive spatial vector data (SVD) with their changeable data schemas, agile scalability, and fast query response time. Spatial queries are basic operations in geospatial databases. According to Green information technology, an efficient spatial index can accelerate query processing and save power consumption for ubiquitous spatial applications. Current solutions tend to pursue it by indexing spatial objects with space-filling curves or geohash on NoSQL databases. As for the performance-wise R-tree family, they are mainly used in slow disk-based spatial access methods on NoSQL databases that incur high loading and searching costs. Therefore, performing spatial queries efficiently with the R-tree family on NoSQL databases remains a challenge. In this paper, an in-memory balanced and distributed R*-tree index named the BDRST index is proposed and implemented on HBase for efficient spatial-query processing of massive SVD. The BDRST index stores and distributes serialized R*-trees to HBase regions in association with SVD partitions in the same table. Moreover, an efficient optimized server-side parallel processing framework is presented for real-time R*-tree instantiation and query processing. Through extensive experiments on real-world land-use data sets, the performance of our method is tested, including index building, index quality, spatial queries, and applications. Our proposed method outperforms other state-of-the-art solutions, saving between 27.36% and 95.94% on average execution time for the above operations. Experimental results show the capability of the BDRST index to support spatial queries over large-scale SVD, and our method provides a solution for efficient sustainability research that involves massive georeferenced data.

Creation of column-oriented NoSQL databases automatically in Big Data environments and its impact on energy consumption

This study investigates the automatic creation of column-oriented NoSQL databases in Big Data environments and their impact on energy consumption. Traditional row-oriented databases face limitations in handling large volumes of data, resulting in slower query response times and energy inefficiencies. In contrast, column-oriented NoSQL databases store data in columns, enabling efficient compression, retrieval, and query processing. Innovative techniques are employed to automatically create these databases, optimizing performance and minimizing manual intervention. Storing data in a columnar format reduces storage requirements and power consumption while improving data locality and reducing I/O operations. This study emphasizes the benefits of adopting column-oriented NoSQL databases, including improved performance, scalability, and energy efficiency in Big Data environments.

Metrics-Based Comparison of OWL and XML for Representing and Querying Cognitive Radio Capabilities

Collaborative spectrum access requires wireless devices to perform spectrum-related tasks (such as sensing) on request from other nodes. Thus, while joining the network, they need to inform neighboring devices and/or the central coordinator of their capabilities. During the operational phase, nodes may request other permissions from the the controller, like the opportunity to transmit according to the current policies and spectrum availability. To achieve such coordinated behavior, all associated devices within the network need a language for describing radio capabilities, requests, scenarios, policies, and spectrum availability. In this paper, we present a thorough comparison of the use of two candidate languages—Web Ontology Language (OWL) and eXtensible Markup Language (XML)—for such purposes. Towards this goal, we propose an evaluation method for automating quantitative comparisons with metrics such as precision, recall, device registration, and the query response time. The requests are expressed in both SPARQL Protocol and RDF Query Language (SPARQL) and XML Query Language (XQuery), whereas the device capabilities are expressed in both OWL and XML. The evaluation results demonstrate the advantages of using OWL semantics to improve the quality of matching results over XML. We also discuss how the evaluation method can be applicable to other scenarios where knowledge, datasets, and queries require richer expressiveness and semantics.

Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses

Data warehousing gives frameworks and means for enterprise administrators to methodically prepare, comprehend, and utilize the data to improve strategic decision-making skills. One of the principal challenges to data warehouse designers is fragmentation. Currently, several fragmentation approaches for data warehouses have been developed since this technique can decrease the OLAP (online analytical processing) query response time and it provides considerable benefits in table loading and maintenance tasks. In this paper, a horizontal fragmentation method, called FTree, that uses decision trees to fragment data warehouses is presented to take advantage of the effectiveness that this technique provides in classification. FTree determines the OLAP queries with major relevance, evaluates the predicates found in the workload, and according to this, builds the decision tree to select the horizontal fragmentation scheme. To verify that the design is correct, the SSB (star schema benchmark) was used in the first instance; later, a tourist data warehouse was built, and the fragmentation method was tested on it. The results of the experiments proved the efficacy of the method.

Query Response Time Comparison SQL and No SQL for Contact Tracing Application

During the current Covid-19 pandemic, it is necessary to have facilities to track community activities to make it easier to cope with the Covid-19 pandemic situation which is still a threat to the community. Contact tracing application (Contact Tracing) is one solution that can be used in this regard. By utilizing Contact Tracing, people's activities can be recorded when traveling to meet other people so that they can generate a history that can be used to track someone if one of them is infected with Covid-19. With this application, data storage (database) is very important because the application will generate and consume a lot of data. In this study, the researcher attempted to compare the query speed of the common data storage (database) that is usually used, namely Relational Database (MySQL) with Non-Relational Database (MongoDB). The results of the experiments conducted by the researchers resulted in the Non-Relational Database (MongoDB) suitable for use when the data has reached large amounts because the query speed is quite fast and stable. Meanwhile, Relational Database performance is still reliable when the data query needs are only on a small scale.

4SA: OPTIMIZING SPACE FILLING CURVE BASED GRID CELL INDEXING TO SCALABLY MANAGE REMOTELY SENSED IMAGES IN KEY-VALUE DATABASES

Abstract. State-of-the-art remote sensing image management systems adopt scalable databases and employ sophisticated indexing techniques to perform window and containment queries. Many rely on space-filling curve (SFC) based index techniques designed for key-value databases and are predominantly employable for images that are iso-oriented. Critically, these indexes do not consider the high degree of overlap among images that exists in many data sets and the affiliated storage requirements. Specifically, employing an SFC-based grid cell index approach in consort with ground footprint coverage of the images requires storage of a unique image object identification (IOI) for each image in every grid cell where overlap occurs. Such an approach adversely affects both storage and query response times. In response, this paper presents an optimization technique for an SFC-based grid cell space indexing. The optimization is specifically designed for window and containment queries where the region of interest overlaps with at least a 2 × 2 grid of cells. The technique is based on four cell removal steps, thus called “four step algorithm” (4SA). Each step employs a unique spatial configuration to check for continuous spatial extent. If present, the IOI of the target cell is omitted from further consideration. Analysis and experiments on real world and synthetic image data demonstrated that 4SA improved storage demands by 41.3% – 47.8%. Furthermore, in the performed querying experiments, only 42% of IOI elements needed to be processed, thus yielding a 58% productivity gain. The reduction of IOI elements in querying also impacted the CPU execution time (3.0% – 5.2%). The 4SA also demonstrated data scalability and concurrent user scalability in querying large regions by completing the index searching and concurrent user scalability 1.86% – 3.35% faster than when 4SA was not applied.

Analysis of Performance Improvement of Real-time Internet of Things Application Data Processing in the Movie Industry Platform.

The goal of this study is to plan and develop complete strategies to improve the performance of film industry. The primary objectives of this study are to investigate a dataset generated by a IoT application and the nature of the data forms obtained, the speed of the data arriving rate, and the required query response time and to list the issues that the current film industry faces when attempting to handle IoT applications in real time. Finally, in film industry platforms, high performance with varied stream circulation levels of real-time IoT application information was realized. In this study, we proposed three alternative methods on top of the Storm platform, nicknamed Re-Storm, to improve the performance of IoT application data. Three different proposed strategies are (1) data stream graph optimization framework, (2) energy-efficient self-scheduling strategy, and (3) real-time data stream computing with memory DVFS. The work proposed a methodology for dealing with heterogeneous traffic-aware incoming rate of data streams Re-Storm at multiple traffic points, resulting in a short response time and great energy efficiency. It is divided into three parts, the first of which is a scientific model for fast response time and great energy efficiency. The distribution of resources is then considered using DVFS approaches, and successful optimum association methods are shown. Third is self-allocation of worker nodes towards optimizing DSG using hot swapping and making the span minimization technique. Furthermore, the testing findings suggest that Re-Storm outperforms Storm by 20–30% for real-time streaming data of IoT applications. This research focuses on high energy efficiency, short reaction time, and managing data stream traffic arrival rate. A model for a specific phase of data coming via IoT and real-time computing devices was built on top of the Storm platform. There is no need to change any software approach or hardware component in this design, but only merely add an energy-efficient and traffic-aware algorithm. The design and development of this algorithm take into account all of the needs of the data produced by IoT applications. It is an open-source platform with less prerequisites for addressing a more sophisticated big data challenge.

Informatization of Accounting System of Electric Power Enterprises Based on Sensor Monitoring and Cloud Computing

In order to promote, restrict, regulate, and balance the accounting work of electric power enterprises, the author proposes an electric power enterprise accounting system based on sensor monitoring and cloud computing. Through the method of integrating information technology on the basis of accounting computerization, through a sensor and modular design analysis, an accounting information management system model is formed. The accounting information management system based on cloud computing combines accounting and management functions. The system test results show that the accounting information management system is in a state of 480 concurrent users, the average response time of a data query is 4.3 seconds, and the average response time of a data access is 5.1 seconds. The average value of the execution data scale per second is 103.52. Conclusion. In order to further improve the accounting software of electric power enterprises, develop management accounting software, and effectively perform the functions of management accounting.

Monitoring of Thermal Comfort and Air Quality for Sustainable Energy Management inside Hospitals Based on Online Analytical Processing and the Internet of Things.

There is a need to ensure comfortable conditions for hospital staff and patients from the point of view of thermal comfort and air quality so that they do not affect their performance. We consider the need for hospital employees and patients to enjoy conditions of greater well-being during their stay. This is understood as a comfortable thermal sensation and adequate air quality, depending on the task they are performing. The contribution of this article is the formulation of the fundamentals of a system and platform for monitoring thermal comfort and Indoor Air Quality (IAQ) in hospitals, based on an Internet of Things platform composed of a low-cost sensor node network that is capable of measuring critical variables such as humidity, temperature, and Carbon Dioxide (CO2). As part of the platform, a multidimensional data model with an On-Line Analytical Processing (OLAP) approach is presented that offers query flexibility, data volume reduction, as well as a significant reduction in query response times. The experimental results confirm the suitability of the platform’s data model, which facilitates operational and strategic decision making in complex hospitals.

Performance benchmark on semantic web repositories for spatially explicit knowledge graph applications

Knowledge graph has become a cutting-edge technology for linking and integrating heterogeneous, cross-domain datasets to address critical scientific questions. As big data has become prevalent in today's scientific analysis, semantic data repositories that can store and manage large knowledge graph data have become critical in successfully deploying spatially explicit knowledge graph applications. This paper provides a comprehensive evaluation of the popular semantic data repositories and their computational performance in managing and providing semantic support for spatial queries. There are three types of semantic data repositories: (1) triple store solutions (RDF4j, Fuseki, GraphDB, Virtuoso), (2) property graph databases (Neo4j), and (3) an Ontology-Based Data Access (OBDA) approach (Ontop). Experiments were conducted to compare each repository's efficiency (e.g., query response time) in handling geometric, topological, and spatial-semantic related queries. The results show that Virtuoso achieves the overall best performance in both non-spatial and spatial-semantic queries. The OBDA solution, Ontop, has the second-best query performance in spatial and complex queries and the best storage efficiency, requiring the least data-to-RDF conversion efforts. Other triple store solutions suffer from various issues that cause performance bottlenecks in handling spatial queries, such as inefficient memory management and lack of proper query optimization.

Performance Analysis of Storage Systems in Edge Computing Infrastructures

Edge computing constitutes a promising paradigm of managing and processing the massive amounts of data generated by Internet of Things (IoT) devices. Data and computation are moved closer to the client, thus enabling latency- and bandwidth-sensitive applications. However, the distributed and heterogeneous nature of the edge as well as its limited resource capabilities pose several challenges in implementing or choosing an efficient edge-enabled storage system. Therefore, it is imperative for the research community to contribute to the clarification of the purposes and highlight the advantages and disadvantages of various edge-enabled storage systems. This work aspires to contribute toward this direction by presenting a performance analysis of three different storage systems, namely MinIO, BigchainDB, and the IPFS. We selected these three systems as they have been proven to be valid candidates for edge computing infrastructures. In addition, as the three evaluated systems belong to different types of storage, we evaluated a wide range of storage systems, increasing the variability of the results. The performance evaluation is performed using a set of resource utilization and Quality of Service (QoS) metrics. Each storage system is deployed and installed on a Raspberry Pi (small single-board computers), which serves as an edge device, able to optimize the overall efficiency with minimum power and minimum cost. The experimental results revealed that MinIO has the best overall performance regarding query response times, RAM consumption, disk IO time, and transaction rate. The results presented in this paper are intended for researchers in the field of edge computing and database systems.

Multi-Objective Big Data View Materialization Using Improved Strength Pareto Evolutionary Algorithm

Big data refers to the enormous heterogeneous data being produced at a brisk pace by a large number of diverse data generating sources. Since traditional data processing technologies are unable to process big data efficiently, big data is processed using newer distributed storage and processing frameworks. Big data view materialization is a technique to process big data queries efficiently on these distributed frameworks. It generates valuable information, which can be used to take timely decisions, especially in cases of disasters. As there are a very large number of big data views, it is not possible to materialize all of them. Therefore, a subset of big data views needs to be selected for materialization, which optimizes the query response time for a given set of workload queries with minimum overheads. This big data view materialization problem, having objectives minimization of the query evaluation cost of a set of workload queries, while simultaneously minimizing the update processing costs of the materialized views, has been addressed using improved strength pareto evolutionary algorithm (SPEA-2) in this paper. The proposed big data view selection algorithm, which is able to compute a set of diverse non-dominated big data views, is shown to perform better that existing big data view selection algorithms..