Relevant bibliographies by topics / DB2, database performance, database workload management

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'DB2, database performance, database workload management'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "DB2, database performance, database workload management"

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Suryana, N., M. S. Rohman, and F. S. Utomo. "PREDICTION BASED WORKLOAD PERFORMANCE EVALUATION FOR DISASTER MANAGEMENT SPATIAL DATABASE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W10 (September 12, 2018): 187–92. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w10-187-2018.

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<p><strong>Abstract.</strong> This paper discusses a prediction based workload performance evaluation implementation during Disaster Management, especially at the response phase, to handle large spatial data in the event of an eruption of the Merapi volcano in Indonesia. Complexity associated with a large spatial database are not the same with the conventional database. This implies that in coming complex work loads are difficult to be handled by human from which needs longer processing time and may lead to failure and undernourishment. Based on incoming workload, this study is intended to predict the associated workload into OLTP and DSS workload performance types. From the SQL statements, it is clear that the DBMS can obtain and record the process, measure the analysed performances and the workload classifier in the form of DBMS snapshots. The Case-Based Reasoning (CBR) optimised with Hash Search Technique has been adopted in this study to evaluate and predict the workload performance of PostgreSQL. It has been proven that the proposed CBR using Hash Search technique has resulted in acceptable prediction of the accuracy measurement than other machine learning algorithm like Neural Network and Support Vector Machine. Besides, the results of the evaluation using confusion matrix has resulted in very good accuracy as well as improvement in execution time. Additionally, the results of the study indicated that the prediction model for workload performance evaluation using CBR which is optimised by Hash Search technique for determining workload data on shortest path analysis via the employment of Dijkstra algorithm. It could be useful for the prediction of the incoming workload based on the status of the predetermined DBMS parameters. In this way, information is delivered to DBMS hence ensuring incoming workload information that is very crucial to determine the smooth works of PostgreSQL.</p>
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Zheng, Shuai, Fusheng Wang, and James Lu. "Enabling Ontology Based Semantic Queries in Biomedical Database Systems." International Journal of Semantic Computing 08, no. 01 (March 2014): 67–83. http://dx.doi.org/10.1142/s1793351x14500032.

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There is a lack of tools to ease the integration and ontology based semantic queries in biomedical databases, which are often annotated with ontology concepts. We aim to provide a middle layer between ontology repositories and semantically annotated databases to support semantic queries directly in the databases with expressive standard database query languages. We have developed a semantic query engine that provides semantic reasoning and query processing, and translates the queries into ontology repository operations on NCBO BioPortal. Semantic operators are implemented in the database as user defined functions extended to the database engine, thus semantic queries can be directly specified in standard database query languages such as SQL and XQuery. The system provides caching management to boosts query performance. The system is highly adaptable to support different ontologies through easy customizations. We have implemented the system DBOntoLink as an open source software, which supports major ontologies hosted at BioPortal. DBOntoLink supports a set of common ontology based semantic operations and have them fully integrated with a database management system IBM DB2. The system has been deployed and evaluated with an existing biomedical database for managing and querying image annotations and markups (AIM). Our performance study demonstrates the high expressiveness of semantic queries and the high efficiency of the queries.
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Van Aken, Dana, Dongsheng Yang, Sebastien Brillard, Ari Fiorino, Bohan Zhang, Christian Bilien, and Andrew Pavlo. "An inquiry into machine learning-based automatic configuration tuning services on real-world database management systems." Proceedings of the VLDB Endowment 14, no. 7 (March 2021): 1241–53. http://dx.doi.org/10.14778/3450980.3450992.

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Modern database management systems (DBMS) expose dozens of configurable knobs that control their runtime behavior. Setting these knobs correctly for an application's workload can improve the performance and efficiency of the DBMS. But because of their complexity, tuning a DBMS often requires considerable effort from experienced database administrators (DBAs). Recent work on automated tuning methods using machine learning (ML) have shown to achieve better performance compared with expert DBAs. These ML-based methods, however, were evaluated on synthetic workloads with limited tuning opportunities, and thus it is unknown whether they provide the same benefit in a production environment. To better understand ML-based tuning, we conducted a thorough evaluation of ML-based DBMS knob tuning methods on an enterprise database application. We use the OtterTune tuning service to compare three state-of-the-art ML algorithms on an Oracle installation with a real workload trace. Our results with OtterTune show that these algorithms generate knob configurations that improve performance by 45% over enterprise-grade configurations. We also identify deployment and measurement issues that were overlooked by previous research in automated DBMS tuning services.
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Raza, Basit, Yogan Jaya Kumar, Ahmad Kamran Malik, Adeel Anjum, and Muhammad Faheem. "Performance prediction and adaptation for database management system workload using Case-Based Reasoning approach." Information Systems 76 (July 2018): 46–58. http://dx.doi.org/10.1016/j.is.2018.04.005.

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Memon, Muhammad Qasim, Jingsha He, Aasma Memon, Khurram Gulzar Rana, and Muhammad Salman Pathan. "Query Processing for Time Efficient Data Retrieval." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 3 (March 1, 2018): 784. http://dx.doi.org/10.11591/ijeecs.v9.i3.pp784-788.

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<p class="TTPAbstract">In database management system (DBMS) retrieving data through structure query language is an essential aspect to find better execution plan for performance. In this paper, we incorporated database objects to optimize query execution time and its cost by vanishing poorly SQL statements. We proposed a method of evolving and inserting database constraints as database objects embedded with queries either to add them for the sake of transactions required by user to detect those queries for the betterment of performance. We took analysis on several databases while processing queries itself and assimilate real time database workload with the bunch of transactions are invoked in comparison with tuning approaches. These database objects are coded in procedural language environment pertaining rules to make it worth and are merged into queries offering improved execution plan.</p>
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Çelikyürek, Hasan, Kadir Karakuş, and Murat Kara. "Hayvancılık İşletmelerinde Kayıtların Veri Tabanlarında Saklanması ve Değerlendirilmesi." Turkish Journal of Agriculture - Food Science and Technology 7, no. 12 (December 14, 2019): 2089. http://dx.doi.org/10.24925/turjaf.v7i12.2089-2094.2793.

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The data stored for a long time in livestock enterprises will play a crucial role in increasing the productivity in animal production, revealing animal breeding values, meeting qualified breeding needs, making effective breeding organizations, obtaining high income, determining the animals to be kept or as a breeder. Among the important technical data kept in livestock enterprises; ram, bull, and goat and their reproduction, growth-development, yield records (animal weight and wool yield in small ruminants, body weight gain, feed consumption, lactation and milk yield), reproductive performance measures, slaughter and carcass dimensions and characteristics records such as meat quality, animal diseases and vaccination practices can be shown as important technical data in livestock enterprises. Issues such as followed animals and storing identifying information of the animals from this data in the database are being made compulsory for conformity program of Turkey with the European Union by the rule number 27137 “Regulation on the identification, registration and monitoring of sheep and goat type of animals” that published in the official newspaper by Agriculture and Forestry Ministry on 10.02.2009. Nowadays, database software such as MySQL, MS SQL, Postrage SQL, Oracle, Firebird, IBM DB2 and MS Access are used in order to obtain healthy data and store the data safely. Knowledge of the use and cost of this database software and Database Management Systems (DBMS) is important for the enterprise. In this study, it is aimed to give information about the software that adds value to the enterprise and their costs of the operations on enterprise.
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Gorbenko, Anatoliy, and Olga Tarasyuk. "EXPLORING TIMEOUT AS A PERFORMANCE AND AVAILABILITY FACTOR OF DISTRIBUTED REPLICATED DATABASE SYSTEMS." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 4 (November 27, 2020): 98–105. http://dx.doi.org/10.32620/reks.2020.4.09.

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A concept of distributed replicated data storages like Cassandra, HBase, MongoDB has been proposed to effectively manage the Big Data sets whose volume, velocity, and variability are difficult to deal with by using the traditional Relational Database Management Systems. Trade-offs between consistency, availability, partition tolerance, and latency are intrinsic to such systems. Although relations between these properties have been previously identified by the well-known CAP theorem in qualitative terms, it is still necessary to quantify how different consistency and timeout settings affect system latency. The paper reports results of Cassandra's performance evaluation using the YCSB benchmark and experimentally demonstrates how to read latency depends on the consistency settings and the current database workload. These results clearly show that stronger data consistency increases system latency, which is in line with the qualitative implication of the CAP theorem. Moreover, Cassandra latency and its variation considerably depend on the system workload. The distributed nature of such a system does not always guarantee that the client receives a response from the database within a finite time. If this happens, it causes so-called timing failures when the response is received too late or is not received at all. In the paper, we also consider the role of the application timeout which is the fundamental part of all distributed fault tolerance mechanisms working over the Internet and used as the main error detection mechanism here. The role of the application timeout as the main determinant in the interplay between system availability and responsiveness is also examined in the paper. It is quantitatively shown how different timeout settings could affect system availability and the average servicing and waiting time. Although many modern distributed systems including Cassandra use static timeouts it was shown that the most promising approach is to set timeouts dynamically at run time to balance performance, availability and improve the efficiency of the fault-tolerance mechanisms.
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Fan, Yimeng, Yu Liu, Haosong Chen, and Jianlong Ma. "Data Mining-based Design and Implementation of College Physical Education Performance Management and Analysis System." International Journal of Emerging Technologies in Learning (iJET) 14, no. 06 (March 29, 2019): 87. http://dx.doi.org/10.3991/ijet.v14i06.10159.

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The purpose of this paper was to effectively apply data mining technology to sci-entifically analyze the students' physical education (PE) performance so as to serve the physical teaching. The methodology adopted in this paper was to apply ASP.NET 3-layer architecture and design and implement college PE performance management and analysis system under the premise of fully analyzing the system requirements based on Visual Studio2008 software development platform and using SQL Server 2005 database platform. Based on data mining technology, students' PE performances were analyzed, and decision tree algorithm was used to make valuable judgments on student performance. The results indicated that applying computer technology to the management and analysis of college PE per-formance can effectively reduce the teaching and managing workload of PE teachers so that the teachers concentrate more on the quality of physical educa-tion.
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Wang, Chenxiao, Zach Arani, Le Gruenwald, Laurent d'Orazio, and Eleazar Leal. "Re-optimization for Multi-objective Cloud Database Query Processing using Machine Learning." International Journal of Database Management Systems 13, no. 1 (February 28, 2021): 21–40. http://dx.doi.org/10.5121/ijdms.2021.13102.

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In cloud environments, hardware configurations, data usage, and workload allocations are continuously changing. These changes make it difficult for the query optimizer of a cloud database management system (DBMS) to select an optimal query execution plan (QEP). In order to optimize a query with a more accurate cost estimation, performing query re-optimizations during the query execution has been proposed in the literature. However, some of there-optimizations may not provide any performance gain in terms of query response time or monetary costs, which are the two optimization objectives for cloud databases, and may also have negative impacts on the performance due to their overheads. This raises the question of how to determine when are-optimization is beneficial. In this paper, we present a technique called ReOptML that uses machine learning to enable effective re-optimizations. This technique executes a query in stages, employs a machine learning model to predict whether a query re-optimization is beneficial after a stage is executed, and invokes the query optimizer to perform the re-optimization automatically. The experiments comparing ReOptML with existing query re-optimization algorithms show that ReOptML improves query response time from 13% to 35% for skew data and from 13% to 21% for uniform data, and improves monetary cost paid to cloud service providers from 17% to 35% on skewdata.
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Tiwari, Rajeev, Shuchi Upadhyay, Gunjan Lal, and Varun Tanwar. "Project Workflow Management: A Cloud based Solution-Scrum Console." International Journal of Engineering & Technology 7, no. 4 (September 20, 2018): 2457. http://dx.doi.org/10.14419/ijet.v7i4.15799.

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Today, there is a data workload that needs to be managed efficiently. There are many ways for the management and scheduling of processes, which can impact the performance and quality of the product and highly available, scalable web hosting can be a complex and expensive proposition. Traditional web architectures don’t offer reliability. So in this work a Scrum Console is being designed for managing a process which will be hosted on Amazon Web Services (AWS) [2] which provides a reliable, scalable, highly available and high performance infrastructure web application. The Scrum Console Platform facilitates the collaboration of various members of a team to manage projects together. The Scrum Console Platform has been developed using JSP, Hibernate & Oracle 12c Enterprise Edition Database. The Platform is deployed as a web application on AWS Elastic Beanstalk which automates the deployment, management and monitoring of the application while relying on the underlying AWS resources such EC2, S3, RDS, CloudWatch, autoscaling, etc.
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Dissertations / Theses on the topic "DB2, database performance, database workload management"

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Nilsson, Victor. "Evaluating Mitigations For Meltdown and Spectre : Benchmarking performance of mitigations against database management systems with OLTP workload." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15254.

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With Spectre and Meltdown out in the public, a rushed effort was made to patch these vulnerabilities by operating system vendors. However, with the mitigations against said vulnerabilities there will be some form of performance impact. This study aims to find out how much of an impact the software mitigations against Spectre and Meltdown have on database management systems during an online transaction processing workload. An experiment was carried out to evaluate two popular open-source database management systems and see how they were affected before and after the software mitigations against Spectre and Meltdown was applied. The study found that there is an average of 4-5% impact on the performance when the software mitigations is applied. The study also compared the two database management systems with each other and found that PostgreSQL can have a reduced performance of about 27% when both a hypervisor and the operating system is patched against Spectre and Meltdown.
När Spectre och Meltdown tillkännagavs gjordes en snabb insats för att korrigera dessa sårbarheter av operativsystemleverantörer. Men med mildringarna mot dessa sårbarheter kommer det att finnas någon form av prestationspåverkan. Denna studie syftar till att ta reda på hur mycket av en påverkan uppdateringarna mot Spectre och Meltdown har på databashanteringssystem under en online-transaktionsbehandlings arbetsbelastning. Ett experiment gjordes för att utvärdera två populära databashanteringssystem baserad på fri mjukvara och se hur de påverkades före och efter att uppdateringarna mot Spectre och Meltdown applicerats i en Linux maskin. Studien fann att det i genomsnitt är 4–5% påverkan på prestandan när uppdateringarna tillämpas. Studien jämförde också de två databashanteringssystemen med varandra och fann att PostgreSQL kan ha en reducerad prestanda på cirka 27% när både det virtuella maskinhanteringssystemet och operativsystemet är uppdaterad mot Spectre och Meltdown.
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Meng, Yabin. "SQL Query Disassembler: An Approach to Managing the Execution of Large SQL Queries." Thesis, 2007. http://hdl.handle.net/1974/701.

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In this thesis, we present an approach to managing the execution of large queries that involves the decomposition of large queries into an equivalent set of smaller queries and then scheduling the smaller queries so that the work is accomplished with less impact on other queries. We describe a prototype implementation of our approach for IBM DB2™ and present a set of experiments to evaluate the effectiveness of the approach.
Thesis (Master, Computing) -- Queen's University, 2007-09-17 22:05:05.304
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Guo, Junjie. "Design, implementation and performance tests for query sampling in DB2 /." 2004.

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Thesis (M.Sc.)--York University, 2004. Graduate Programme in Computer Science.
Typescript. Includes bibliographical references (leaves 112-116). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: LINK NOT YET AVAILABLE.
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Books on the topic "DB2, database performance, database workload management"

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Inmon, William H. Optimizing performance in DB2 software. Englewood Cliffs, N.J: Prentice Hall, 1988.

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Silverberg, David. DB2: Performance, design, and implementation. New York: McGraw-Hill, 1992.

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Alur, Nagraj. DB2 UDB/WebSphere performance tuning guide. San Jose, Calif: IBM Corp., International Tecnical Support Organization, 2002.

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Susan, Lawson, ed. DB2 high performance design and tuning. Upper Saddle River, NJ: Prentice Hall PTR, 2001.

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DB2 9 for z/OS performance topics. [United States?]: IBM, International Technical Support Organization, 2007.

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Inmon, William H. DB2: Maximizing performance of online production systems. Wellesley, Mass: QED Information Sciences, 1989.

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DB2 performance and development guide. New York: Van Nostrand Reinhold, 1991.

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Lawson, Susan. DB2 for z/OS high performance design and tuning. 2nd ed. Upper Saddle River, NJ: Prentice Hall Professional Technical Reference, 2005.

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Rudd, Anthony S. Implementing practical DB2 applications. London: Springer, 1996.

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Rudd, Anthony S. Implementing practical DB2 applications. New York: E. Horwood, 1990.

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Book chapters on the topic "DB2, database performance, database workload management"

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Maghawry, Eman A., Rasha M. Ismail, Nagwa L. Badr, and Mohamed F. Tolba. "Workload Management Systems for the Cloud Environment." In Handbook of Research on Machine Learning Innovations and Trends, 94–113. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2229-4.ch005.

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Workload Management is a performance management process in which an autonomic database management system on a cloud environment efficiently makes use of its virtual resources. Workload management for concurrent queries is one of the challenging aspects of executing queries over the cloud. The core problem is to manage any unpredictable overload with respect to varying resource capabilities and performances. This chapter proposes an efficient workload management system for controlling the queries execution over a cloud. The chapter presents architecture to improve the query response time. It handles the user's queries then selecting the suitable resources for executing these queries. Furthermore, managing the life cycle of virtual resources through responding to any load that occurs on the resources. This is done by dynamically rebalancing the queries distribution load across the resources in the cloud. The results show that applying this Workload Management System improves the query response time by 68%.
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Indraratne, Harith, and Gábor Hosszú. "Fine-Grained Data Access for Networking Applications." In Encyclopedia of Multimedia Technology and Networking, Second Edition, 568–73. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-014-1.ch076.

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Current-day network applications require much more secure data storages than anticipated before. With millions of anonymous users using same networking applications, security of data behind the applications have become a major concern of database developers and security experts. In most security incidents, the databases attached to the applications are targeted, and attacks have been made. Most of these applications require allowing data manipulation at several granular levels to the users accessing the applications—not just table and view level, but tuple level. A database that supports fine-grained access control restricts the rows a user sees, based on his/her credentials. Generally, this restriction is enforced by a query modification mechanism automatically done at the database. This feature enables per-user data access within a single database, with the assurance of physical data separation. It is enabled by associating one or more security policies with tables, views, table columns, and table rows. Such a model is ideal for minimizing the complexity of the security enforcements in databases based on network applications. With fine-grained access controls, one can create fast, scalable, and secure network applications. Each application can be written to find the correct balance between performance and security, so that each data transaction is performed as quickly and safely as possible. Today, the database vendors like Oracle 10g, and IBM DB2 provides commercial implementations of fine-grained access control methods, such as filtering rows, masking columns selectively based on the policy, and applying the policy only when certain columns are accessed. The behavior of the fine-grained access control model can also be increased through the use of multiple types of policies based on the nature of the application, making the feature applicable to multiple situations. Meanwhile, Microsoft SQL Server2005 has also come up with emerging features to control the access to databases using fine-grained access controls. Fine-grained access control does not cover all the security issues related to Internet databases, but when implemented, it supports building secure databases rapidly and bringing down the complexity of security management issues.
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Bentayeb, Fadila, Cécile Favre, and Omar Boussaid. "Dynamic Workload for Schema Evolution in Data Warehouses." In Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development, 28–46. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-60566-748-5.ch002.

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A data warehouse allows the integration of heterogeneous data sources for identified analysis purposes. The data warehouse schema is designed according to the available data sources and the users’ analysis requirements. In order to provide an answer to new individual analysis needs, the authors previously proposed, in recent work, a solution for on-line analysis personalization. They based their solution on a user-driven approach for data warehouse schema evolution which consists in creating new hierarchy levels in OLAP (on-line analytical processing) dimensions. One of the main objectives of OLAP, as the meaning of the acronym refers, is the performance during the analysis process. Since data warehouses contain a large volume of data, answering decision queries efficiently requires particular access methods. The main issue is to use redundant optimization structures such as views and indices. This implies to select an appropriate set of materialized views and indices, which minimizes total query response time, given a limited storage space. A judicious choice in this selection must be cost-driven and based on a workload which represents a set of users’ queries on the data warehouse. In this chapter, the authors address the issues related to the workload’s evolution and maintenance in data warehouse systems in response to new requirements modeling resulting from users’ personalized analysis needs. The main issue is to avoid the workload generation from scratch. Hence, they propose a workload management system which helps the administrator to maintain and adapt dynamically the workload according to changes arising on the data warehouse schema. To achieve this maintenance, the authors propose two types of workload updates: (1) maintaining existing queries consistent with respect to the new data warehouse schema and (2) creating new queries based on the new dimension hierarchy levels. Their system helps the administrator in adopting a pro-active behaviour in the management of the data warehouse performance. In order to validate their workload management system, the authors address the implementation issues of their proposed prototype. This latter has been developed within client/server architecture with a Web client interfaced with the Oracle 10g DataBase Management System.
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Conference papers on the topic "DB2, database performance, database workload management"

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Korkmaz, Mustafa, Martin Karsten, Kenneth Salem, and Semih Salihoglu. "Workload-Aware CPU Performance Scaling for Transactional Database Systems." In SIGMOD/PODS '18: International Conference on Management of Data. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3183713.3196901.

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Peck Lee, Sai, and Dzemal Zildzic. "Oracle Database Workload Performance Measurement and Tuning Toolkit." In InSITE 2006: Informing Science + IT Education Conference. Informing Science Institute, 2006. http://dx.doi.org/10.28945/2965.

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Database tuning practice is mainly conducted as a consequence of user’s complaints on the performance. There is a need for a reactive monitoring and tuning tool enabling a real-time overview of the main resource consumers in order to detect and solve performance bottlenecks. With an assessment of the Oracle’s high-availability database, in terms of the main architectural components and their impact on performance, we have developed a Java tool for the efficient and resource-effective tuning of Oracle databases. Our tool, called Workload Performance Monitoring and Tuning (WPMT), enables proactive and reactive database tuning. By combining today’s best monitoring and tuning practices with our metrics management, we have designed a unique approach to illustrate the efficiency of the Oracle database memory segments responsible for handling the workload. This approach consists of developing database memory delta charts, illustrating the efficiency of memory initialization parameters versus component’s workload performance.
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