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With the development of big data technology, the data management of complex applications has become more and more resource intensive. In this paper, we propose an automatic approach (DRLISA) to achieve NoSQL database index selection. For different workloads, we automatically select its corresponding indexes and parameters which can totally improve the database performance. Our DRLISA establishes an optimal index by building a deep reinforcement learning model which is able to adapt the dynamic change of workloads. We conducted our experiments in five aspects (the impact of data manipulation, the impact of operation count, comparison with random selection, comparison with existing method and the robustness of DRLISA) using the open source benchmark, YCSB. The experimental results showed that DRLISA has a high efficient index recommendation under the dynamic workloads.

The aim of this thesis was to compare the performance of two relational database management systems PostgreSQL and MySQL. For the purpose of the study a relational database was designed and a piece of software was implemented to connect desktop application with the database system. This software shall also create entities and relations between them in desired empty schemes on servers for databases. There has also been implemented a desktop application in Java programming language, that allows browsing data stored in database and performing the tests of database performance. Tests addressed basic operations of adding, collecting, updating and deleting data. Thisthesis makes a hypothesis "PostgreSQL is more efficient for desktop application while loaded with small data, in that case 1000 of queries", that was confirmed by achieved results from tests performed.

A large body of knowledge on database tuning is available in the form of natural language text. We propose to leverage natural language processing (NLP) to make that knowledge accessible to automated tuning tools. We describe multiple avenues to exploit NLP for database tuning, and outline associated challenges and opportunities. As a proof of concept, we describe a simple prototype system that exploits recent NLP advances to mine tuning hints from Web documents. We show that mined tuning hints improve performance of MySQL and Postgres on TPC-H, compared to the default configuration.

Database tuning is crucial step to enhance the performance of the application software. There are many tools available in Microsoft to evaluate the performance of the stored procedures and identify them. This paper presents a comparative performance of actual and tuned sample stored procedures in SQL Server 2008 R2 of Application software (Microsoft). The results showed there is a marginal gain in the efficiency after the database tuning.

Managing large chain of Hotels and ERP database comprises of core areas such as HRMS & PIP.HRMS (Human Resource Management System), which further includes areas such as Soft Joining, Promotion, Transfer, Confirmation, Leave Attendance and Exit, etc. PIP (Payroll Information Portal), wherein employees can view their individual Salary details, submit investment declaration, Reimbursement claim & CTC structuring, etc. Management of Large Chain of Hotels and ERP Database in AWS Cloud involves continuous monitoring with regards to the areas such as Performance of resource usages and optimization techniques relating to the use of PL/SQL. High Availability (HA) of data is accomplished through the Backup and Recovery mechanism and security of the data by Encryption & Decryption mechanism.

Processing data in an institution or organization has become an activity or routine for each meeting. In processing data, of course there are various kinds of problems, ranging from missing data, either because of an error in entering data or the result of a system failure. To overcome these problems, in an agency or company must have data backup, so as to create problems that occur in the data, the institution still has data backup. To call, another method of database replication is used. From the test results obtained data on the value of RTA (Round Trip Average) that is the time required for one time sending and receiving data from the replication database is 5426.22 ms and for compilation service diagrams a query attack occurs with a percentage of 29% in the database applications that produce 1774.45 ms results and for service diagrams at a query attack percentage of 29% a single database estimates the server down .

One of the most important applications of the intelligent operation and maintenance of a cloud database is its trend prediction of key performance indicators (KPI), such as disk use, memory use, etc. We propose a method named AutoPA4DB (Auto Prophet and ARIMA for Database) to predict the trend of the KPIs of the cloud database based on the Prophet model and the ARIMA model. Our AutoPA4DB method includes data preprocessing, model building, parameter tuning and optimization. We employ the weighted MAPE coverage to measure its accuracy and use 6 industrial datasets including 10 KPIs to compare the AutoPA4DB method with other three time-series trend prediction algorithms. The experimental results show that our AutoPA4DB method performs best in predicting monotonic variation data, e.g.disk use trend prediction. But it is unstable in predicting oscillatory variation data; for example, it is acceptable in memory use trend prediction but has poor accuracy in predicting the number of database connection trends.

Oracle, MSSQL, MySQL and PostgreSQL are four of the most popular relational databases. They are often used in internet applications. This paper aims to compare the efficiency of these technologies in terms of speed using virtualization with Docker. No publications that include virtualization were found among previous papers. After review of the literature, it was hypothesized that the Oracle engine would be the fastest. During the research, a series of experiments was carried out using the application, in which tests for measuring the time of instruction execution were implemented. Each query was measured 100 times and the first measurement was rejected. The obtained results confirmed the hypothesis about the superiority of the Oracle database. As in previous studies, it proved to be the fastest, also using virtualization.

One of the common problems at the intersection of geographical information science and transportation science is the estimation of origin-destination (OD) matrices. The emergence of sensor technologies offers unprecedented opportunities in this regard since massive amounts of traffic data can be collected in an easy way. Researchers and practitioners need to choose a suitable DataBase Management System (DBMS) among alternatives, such that storing and analysing traffic data to estimate the OD matrix is feasible. The aim of this paper is to compare the performance of two such notable DBMSs, PostgreSQL and MongoDB, in the context of OD matrix estimation. The experiments are carried out on New York City’s openly available taxi data on two different polygon sets: taxi zones and census blocks. These polygon layers consist of 263 and 38794 features respectively. The results suggest that Postgres outperforms MongoDB by generating the OD matrix instantly. The run time of MongoDB varies depending on the analysed time interval and follows a trip demand curve. As there are more trips involved in the generation of the OD matrix, so does the execution time increases in MongoDB. On the other hand, the query results are the same. Finally, the origin points of the taxi trips are visualised in QGIS using the ‘TimeManager’ plugin, and results are presented through a web-interface.

National Institute of Aeronautics and Space of Indonesia is obliged to organize remote sensing data storage and distribution through National Remote Sensing Data Bank (BDPJN). BDPJN is a node of the remote sensing data network in a spatial national data network system. BDPJN has the primary function of collecting, storing, distributing metadata and remote sensing data for Indonesia territory. Evaluation of data operation management is needed to guarantee the continuity of the remote sensing data service at BDPJN. It has to ensure that: the catalog can be accessed by users, the catalog is continuously updated with data stored in the BDPJN storage system, and the data is available when needed. This research is purposed to evaluate data operation management on BDPJN. It is implemented by measuring the maturity level of data operations management activities using the CMMI and DMBOK frameworks. The results indicated that BDPJN has ten activities that have met an expected level of maturity. However, five of fifteen activities have below maturity level 3. Three activities at maturity level 1 are Monitor and Tune Database Performance, Plan for Data Retention, Archive, Retain, and Purge Data. The two others are Define the Data Technology Architecture and Evaluate Data Technology, which is at maturity level 2. Recommendations have been arranged based on references from DMBOK for these five activities.

This paper presents the Outer-Tuning framework, which aims to support the (semi) automatic tuning of relational database systems through a domain-specific ontology. Ontologies have shown themselves to be increasingly promising, adding semantics and standardizing the different terms used in a domain. Thereby, our framework seeks to explain and make explicit the tuning heuristics reasoning while enabling the evaluation of new ontology-inferred methods. In this paper we focus on the main aspects of the Outer-Tuning component-based architecture. We also give an overview of our tool in practice. Finally, we show two useful extensions, concerning new DBMSs and a way of dockerizing into a container.

Database knob tuning is important to achieve high performance (e.g., high throughput and low latency). However, knob tuning is an NP-hard problem and existing methods have several limitations. First, DBAs cannot tune a lot of database instances on different environments (e.g., different database vendors). Second, traditional machine-learning methods either cannot find good configurations or rely on a lot of high-quality training examples which are rather hard to obtain. Third, they only support coarse-grained tuning (e.g., workload-level tuning) but cannot provide fine-grained tuning (e.g., query-level tuning). To address these problems, we propose a query-aware database tuning system QTune with a deep reinforcement learning (DRL) model, which can efficiently and effectively tune the database configurations. QTune first featurizes the SQL queries by considering rich features of the SQL queries. Then QTune feeds the query features into the DRL model to choose suitable configurations. We propose a Double-State Deep Deterministic Policy Gradient (DS-DDPG) model to enable query-aware database configuration tuning, which utilizes the actor-critic networks to tune the database configurations based on both the query vector and database states. QTune provides three database tuning granularities: query-level, workload-level, and cluster-level tuning. We deployed our techniques onto three real database systems, and experimental results show that QTune achieves high performance and outperforms the state-of-the-art tuning methods.

OBJECTIVE The main purpose of our study was to subjectively assess the quality of a paediatric intensive care unit (PICU) database according to the Directory of Clinical Databases (DoCDat) criteria. DESIGN AND SETTING A survey was conducted between April 1 and June 15, 2018, among the Sainte Justine PICU research group. POPULATION Every member of this group whose research activity required the use of the database and/or who was involved in the development/validation of the database. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS All 10 research team members (one Information Technology specialist, one junior medical student, and eight clinician researchers) who used the high-resolution database fulfilled the survey (100% response rate). The median quality level of the Sainte Justine PICU database across all the 10 criteria was 3 (2-4), rated on a 1 (worst) to 4 (best) numeric scale. When compared with previously assessed databases through the DoCDat criteria, we found that the Sainte Justine PICU database performance was similar. CONCLUSIONS The PICU high-resolution database appeared of good quality when subjectively assessed by the DoCDat criteria. Further validation procedures are mandatory. We suggest that data quality assessment and validation procedures should be reported when creating a new database.

This study aims to compare Firebase Realtime Database and MySQL performance as Database Management System for our Toddlers Daily Nutritional Needs Mobile Application. The comparison is in terms of Database response time. Their performance is compared using a common CRUD operations. Each operations data is analyzed using Wilcoxon Signed-Rank test. Our comparison results confirm that Firebase Realtime Database is more suitable as our Daily Nutritional Needs Mobile Application Database Management System since it could perform better response time. The key features match our needs to provide Realtime Data and it is also help simplify the process of developing applications.

Introduction. We have seen significant advances in the state of the art in natural language processing (NLP) over the past few years [20]. These advances have been driven by new neural network architectures, in particular the Transformer model [19], as well as the successful application of transfer learning approaches to NLP [13]. Typically, training for specific NLP tasks starts from large language models that have been pre-trained on generic tasks (e.g., predicting obfuscated words in text [5]) for which large amounts of training data are available. Using such models as a starting point reduces task-specific training cost as well as the number of required training samples by orders of magnitude [7]. These advances motivate new use cases for NLP methods in the context of databases.

Database access logs are the starting point for many forms of database administration, from database performance tuning, to security auditing, to benchmark design, and many more. Unfortunately, query logs are also large and unwieldy, and it can be difficult for an analyst to extract broad patterns from the set of queries found therein. Clustering is a natural first step towards understanding the massive query logs. However, many clustering methods rely on the notion of pairwise similarity, which is challenging to compute for SQL queries, especially when the underlying data and database schema is unavailable. We investigate the problem of computing similarity between queries, relying only on the query structure. We conduct a rigorous evaluation of three query similarity heuristics proposed in the literature applied to query clustering on multiple query log datasets, representing different types of query workloads. To improve the accuracy of the three heuristics, we propose a generic feature engineering strategy, using classical query rewrites to standardize query structure. The proposed strategy results in a significant improvement in the performance of all three similarity heuristics.

Write-ahead logging (WAL) has been widely used to provide transactional atomicity in databases, such as SQLite and MySQL/InnoDB. However, the WAL introduces duplicate writes, where changes are recorded in the WAL file and then written to the database file, called checkpointing writes. On the other hand, NAND flash-based SSDs, which have an inherent indirection software layer, called flash translation layer (FTL), become commonplace in modern storage systems. Innovative SSD designs have been proposed to eliminate the WAL overheads by exploiting the FTL, such as providing an atomic write interface or utilizing its address remapping. However, these designs introduce significant performance overheads of maintaining and persisting extra transactional information to guarantee the transactional atomicity or mapping consistency. In this paper, we propose single-write WAL (SW-WAL), a novel cross-layer design, to eliminate WAL-induced duplicate writes on SSDs with minimal overheads. The SSD exposes an address remapping interface to the host, through which the checkpointing writes can be completed without conducting real data writes. To ensure the transactional atomicity and mapping consistency, we make the SSD aware of the transactional writes to the WAL file. Specifically, when transactional data are written to the WAL file, both transactional and mapping semantics are delivered from the host to the SSD and persisted in relevant flash pages as housekeeping metadata without any extra overheads. We implement a prototype of SW-WAL, which runs a popular database SQLite on an emulated NVMe SSD. Experimental results show that SW-WAL improves the database performance by up to 62% compared with original SQLite that bears the WAL overheads and up to 32% compared with the state-of-the-art design that eliminates the WAL overheads.

Writing good SQL is really about database performance. Although tuning of the database can help with performance, bad SQL will impact performance in ways that tuning the database just cannot cure. When the database administrator(s) has done their job, it is time for the training of the users to begin. All relational databases have their quirks, and it is paramount for the users to be aware of these and have the knowledge to either bypass them or create SQL that takes advantage of the database design and features.

The preceding chapters covered a lot of optimization techniques: not only different ways to optimize SQL statements but also how database design affects performance, the importance of working together with application developers, the use of functions, and many other aspects of database performance. This chapter organizes these techniques into a set of guidelines providing a systematic way to develop efficient declarative queries and/or tune/optimize them, as well as choose the right database design.

Nowadays, the widely used NoSQL databases play a fundamental role for big data storage, processing and analysis. However, NoSQL databases usually consume a large amount of computing resources and always run in a low performance state, which seriously impacts the end-to-end quality of service. Therefore, tuning the performance of NoSQL databases via benchmarking tools becomes a critical issue. Unfortunately, it is non-trivial to solve this problem because there exists a considerable number of performance-related configuration parameters from both the databases (application-specific) and the operating system kernel (kernel-specific), and manually tuning methods only consider a small subset of the whole candidate configuration parameters space, leading to a sub-optimal performance. To address this challenge, we design ConfAdvisor, an automatic configuration tuning framework for NoSQL database benchmarking. Specifically, ConfAdvisor treats the database performance as a black-box function of configuration parameters and leverage an online learning method to search the best configurations. Experimental results based on several popular NoSQL databases show that: 1) Through only tuning the kernel-specific parameters can improve database performance by up to 30%, which is often overlooked in previous studies. 2) ConfAdvisor is able to achieve well-tuned configurations with a very few trials, even in a high-dimension configuration space. And compared with default configurations, the well-tuned parameters can improve database performance by up to 88%.

Gone is the time when a Solid-State Drive (SSD) was just a fast drop-in replacement for a Hard-Disk Drive (HDD). Thanks to the NVMe ecosystem, nowadays, SSDs are accessed through specific interfaces and modern I/O frameworks. SSDs have also grown versatile with time and can now support various use cases ranging from cold, high-density storage to hot, low-latency ones. The body of knowledge about building such different devices is mostly available, but it is less than accessible to non-experts. Finding which device variation can better support a given workload also requires deep domain knowledge. This tutorial's first goal is to make these tasks--understanding the design of SSDs and pairing them with the data-intensive workloads they support well--more inviting. The tutorial goes further, however, in that it suggests that a new kind of SSD plays an essential role in post-Moore computer systems. These devices can be co-designed to align their capabilities to an application's requirements. A salient feature of these devices is that they can run application logic besides just storing data. They can thus gracefully scale processing capabilities with the volume of data stored. The tutorial's second goal is thus to establish the design space for co-designed SSDs and show the tools available to hardware, systems, and databases researchers that wish to explore this space.

Current relational database systems are forming the main part of the intelligent information and support system. Data inside are secured, protected by the relational integrity covered by the transactions. Access and management are done by the relational algebra. Significant aspects expressing the quality and performance are the data access techniques themselves. The index is one of the key features ensuring data retrieval performance, however, from the other operations point of view, it brings additional costs. If the suitable index for the query is not present, the whole table must be scanned sequentially resulting in poor performance. If the data fragmentation is present, the problem is even deeper. This paper aims to propose its own techniques based on relevant block identification objects stored in private or shared memory areas. It uses rebalancing methods on the extent granularity. If the tables are to be joined and both indexes are not present, usually, the Hash join method is used. The technique of two-dimensional index mapper is discussed in the paper, as well.

The root cause diagnosis of performance anomaly for online database anomalies is challenging due to diverse types of database engines, different operational modes, and variable anomaly patterns. To relieve database operators from manual anomaly diagnosis and alarm storm, we propose FluxInfer, a framework to accurately and rapidly localize root cause related KPIs for database performance anomaly. It first constructs a Weighted Undirected Dependency Graph (WUDG) to represent the dependency relationships of anomalous KPIs accurately, and then applies a weighted PageRank algorithm to localize root cause related KPIs. The testbed evaluation experiments show that the AC@3, AC@5, and Avg@5 of FluxInfer are 0.90, 0.95, and 0.77, outperforming nine baselines by 64%, 60%, and 53% on average, respectively.

Due to the continuous expansion of telecom operators (TOs) services and subscriber’s base, a number of objects in TO product catalogue (PC) traditionally developed employing some relational database (RD) rapidly increase. This can result in degraded PC database performance in terms of reduced object visibility or slow query response time, which mandates a search for new types of databases that can cope with such challenges. A possible solution to these challenges is the transformation of RD into some graph database (GD), with visualisation and organisation capabilities that can cope with the increasing complexity of PC RDs. Hence, in this paper, a novel implementation concerning the transformation of a set of PC data and functions of the TO RD into ArangoDB GD is proposed. The proposed approach allows the upgrading of PC RD into a higher presentation layer through the implementation of the GD, which enables advanced data analysis and visualization. The developed solution shows that GD with appropriately structured queries, data and links between them, improves the visibility and simplifies the entire search process. Obtained results show that GD outperforms RD in terms of search query response time for different functions executed on an equal amount of database objects.

Digitalization is currently the key factor for progress, with a rising need for storing, collecting, and processing large amounts of data. In this context, NoSQL databases have become a popular storage solution, each specialized on a specific type of data. Next to that, the multi-model approach is designed to combine benefits from different types of databases, supporting several models for data. Despite its versatility, a multi-model database might not always be the best option, due to the risk of worse performance comparing to the single-model variants. It is hence crucial for software engineers to have access to benchmarks comparing the performance of multi-model and single-model variants. Moreover, in the current Big Data era, it is important to have cluster infrastructure considered within the benchmarks. In this paper, we aim to examine how the multi-model approach performs compared to its single-model variants. To this end, we compare the OrientDB multi-model database with the Neo4j graph database and the MongoDB document store. We do so in the cluster setup, to enhance state of the art in database benchmarks, which is not yet giving much insight into cluster-operating database performance.

IoT devices have become necessary today. As technology grows, the need for reliable, secure, and efficient applications has become essential. To have an active IoT service, the performance of the backend databases is critical. Database plays a crucial role in storing and organizing data that supports the IoT applications. The current research will examine the performance of databases for IoT applications.

Cloud vendors provide ready-to-use distributed DBMS solutions as a service. While the provisioning of a DBMS is usually fully automated, customers typically still have to make important design decisions which were traditionally made by the database administrator such as finding an optimal partitioning scheme for a given database schema and workload. In this paper, we introduce a new learned partitioning advisor based on Deep Reinforcement Learning (DRL) for OLAP-style workloads. The main idea is that a DRL agent learns the cost tradeoffs of different partitioning schemes and can thus automate the partitioning decision. In the evaluation, we show that our advisor is able to find non-trivial partitionings for a wide range of workloads and outperforms more classical approaches for automated partitioning design.

Index selection plays a substantial role in database performance by reducing the I/O cost. Existing index advisors apply different heuristic methods to search the large search space of possible attributes for indexing. These heuristic approaches do not have a mechanism to learn about the goodness of the recommended index set. Thus, they might choose the same index set with a low impact on I/O cost reduction. Learning from their decisions can improve the quality of the recommended index set. We believe that Deep Reinforcement Learning (DRL) is a solution to tackle this issue. Using DRL, an index advisor can improve its decision using the feedbacks of its decisions. In this paper, we propose a DRL-index advisor for a cluster database. We describe the major components such as agent, environment, set of actions, the reward function, and other modules. We conclude the paper with open challenges and possible future work.

Benchmarks play a crucial role in database performance evaluation, and have been effectively promoting the development of database management systems. With critical transaction processing requirements of new applications, we see an explosion of innovative database technologies for dealing with highly intensive transaction workloads (OLTP) with the obvious characteristics of sharp dynamics, terrificskewness, high contention, or high concurrency (abbr. DSC2), which can not be well described or evaluated by current standard benchmarks. In this paper, based on the representative SecKill applications, we define a pacakge of workloads simulating intensive transactional processing requirements. And we create a general and flexible benchmark framework PeakBench for evaluating intensive OLTP workloads on databases. We are the first work to have full control on simulating DSC2, especially for the fine granularity control for contention generation. With a comprehensive set of experiments conducted on popular open sourced DBMSs compared with the other representative OLTP benchmarks, we completely demonstrate the usefulness of PeakBench.

Embedded database libraries provide developers with a common and convenient data persistence layer. They are a key component of major mobile operating systems, and are used extensively on interactive devices like smartphones. Database performance affects the response times and resource consumption of millions of smartphone apps and billions of smartphone users. Given their wide use and impact, it is critical that we understand how embedded databases operate in realistic mobile settings, and how they interact with mobile environments. We argue that traditional database benchmarking methods produce misleading results when applied to mobile devices, due to evaluating performance only at saturation. To rectify this, we present PocketData, a new benchmark for mobile device database evaluation that uses typical workloads to produce representative performance results. We explain the performance measurement methodology behind PocketData, and address specific challenges. We analyze the results obtained, and show how different classes of workload interact with database performance. Notably, our study of mobile databases at non-saturated levels uncovers significant latency and energy variation in database workloads resulting from CPU frequency scaling policies called governors—variation that we show is hidden by typical benchmark measurement techniques.

Whenever a new database technology appears, several comparisons also come up to attest that the new database technology is better than the traditional relational one. Even more, an outstanding performance is shown quite often by conducting performance comparisons. This paper attempts to illustrate that these performance comparisons should be taken with a pinch of salt. Revisiting published statements about comparisons between the Neo4j graph database and relational systems, we investigate several causes why relational systems show a worse performance. One possible reason is – among others – applying a default database configuration or configuring the system inadequately. Next, most tests are implemented in a straightforward manner, particularly not considering alternatives or applying useful features. In order to support our findings, we use a PostgreSQL database and implement some scenarios that are commonly used in comparisons. Thereby, we invalidate some stated results about the bad performance of relational systems in those scenarios. Concluding the discussion, we present some general considerations how fairness of comparisons can be improved.

A new methodology is proposed to compare database performance for streams of patient respiratory data from patients in an intensive care unit. New metrics are proposed through which databases may be compared both for this and similar streaming applications in the domain of the Internet of Things. Studies are reported using simulated patient data for four freely available databases. The statistical technique of non-parametric bootstrapping is used to minimise the total running time of the tests. We report mean values and bias corrected and accelerated confidence intervals for each metric and use these to compare the databases. We find that, among the four databases tested, ScaleDB is an optimum database technology when handling between 200 and 800 patients in this application, while PostgreSQL performs best outside of this range. Comparing the non-parametric bootstrapping method to a complete set of tests shows that the two approaches give results differing by a few percent.

Database performance throughput is one of the main measurements of the application effectiveness or a system, in terms of speed and delivery. Higher application efficiency is always a goal for any organization; therefore, finding the way to improve the database performance has priority. There are two main factors that can contribute to performance gain or slowness. Firstly, how well designed the database structure is in terms of data types, referential integrity, and indexes. Secondly, the underlying hardware infrastructure that consists of a server, storage, and network. During the application design lifecycle, the database schema is one area that continuously evolves. There are numerous configurational parameters that need to be adjusted according to database schema design and underlying infrastructure, in which in-memory is an important feature. In-memory database stores all data in physical memory, because of keeping entire data and changes of data in physical memory, the in-memory database provides very short response time and transactional throughput as compared to the disk-based database. This study provides comparison between in-memory and disk-based performance with impact analysis of concurrent users and parallelism using TPC-C benchmarking. A comprehensive benchmark guideline benefits for specific database environment was provided in order to track performance changes while migrating to in-memory from disk-based database. These benchmarking statistics provided comparative standpoint that can be verified during performance bottleneck. This study shows that transitioning from disk-based database to in-memory database decreased the response time and reduced the lock contention, however, requires detailed review for index design changes. Key words: Microsoft Structured Query Language (SQL) Server, in-memory, disk-based, transaction processing performance council (TPC-C), benchmarking, database performance, performance comparison, response time, parallelism, concurrent users.

This paper describes an experimental study regarding NoSQL database performance. Two NoSQL databases were considered (MongoDB and Cassandra), two different workloads (update heavy and mostly read) and several degrees of parallelism. The results refer to throughput performance metric. Statistical analysis performed is referring to throughput results. Conclusions confirm that MongoDB performs better than Cassandra in the context of a mostly read workload, while Cassandra outperformed MongoDB in the context of an update heavy workload where the number of operations was high.

Many organizations today store their critical business information permanently in XML format. XML data can be managed using: XML-Enabled Database (XED) systems which convert and store XML files in traditional database systems; Native XML Database (NXD) systems which store XML data natively using three main storage technologies – text-based, model-based, and schema-based techniques; and Hybrid Database systems which are comprised of both XML-Enabled and Native XML database systems. NXDs are faster than other database technologies because there is no need to convert the format of the data prior to storage. No performance evaluation has been carried out to compare all three storage strategies, hence, this paper reports on the first attempt to evaluate all three storage strategies by using open source products to measure the response time taken for each of the database basic tasks such as database creation, dataset insertion, and data manipulation. The results of the evaluation show that the schema-based storage strategy: performs 3.5 times faster than the other two storage techniques in data insertion; shows very good performance in query processing on small and large datasets; performs 10.33 times faster than text-based, and 7.5 times faster than model-based storage techniques in query processing of large datasets.

NoSQL database management systems are very diverse and are known to evolve very fast. With so many NoSQL database options available nowadays, it is getting harder to make the right choice for certain use cases. Also, even for a given NoSQL database management system, performance may vary significantly between versions. Database performance benchmarking shows the actual performance for different scenarios on different hardware configurations in a straightforward and precise manner. This paper presents a NoSQL database performance study in which two of the most popular NoSQL database management systems (MongoDB and Apache Cassandra) are compared, and the analyzed metric is throughput. Results show that Apache Cassandra outperformes MongoDB in an update heavy scenario only when the number of operations is high. Also, for a read intensive scenario, Apache Cassandra outperformes MongoDB only when both number of operations and degree of parallelism are high.

Although graph databases have great potential to directly represent huge and complex network structures, such as social and logistics networks, they currently do not perform well enough. To achieve a two-orders-of-magnitude performance improvement on graph database queries, the authors propose caching the graph database in distributed GPUs connected with a 10-Gbit Ethernet (10GbE) network. Property read, traverse, and graph search queries are accelerated by 25.9, 252.4, and 383.2 times, respectively, compared to those using Neo4j for a huge graph consisting of 3.2 million nodes whose degree is 100.

Kabakus, Abdullah Talha/0000-0003-2181-4292; Kara, Resul/0000-0001-8902-6837 WOS: 000414049000010 The popularity of NoSQL databases has increased due to the need of (1) processing vast amount of data faster than the relational database management systems by taking the advantage of highly scalable architecture, (2) flexible (schema-free) data structure, and, (3) low latency and high performance. Despite that memory usage is not major criteria to evaluate performance of algorithms, since these databases serve the data from memory, their memory usages are also experimented alongside the time taken to complete each operation in the paper to reveal which one uses the memory most efficiently. Currently there exists over 225 NoSQL databases that provide different features and characteristics. So it is necessary to reveal which one provides better performance for different data operations. In this paper, we experiment the widely used in-memory databases to measure their performance in terms of (1) the time taken to complete operations, and (2) how efficiently they use memory during operations. As per the results reported in this paper, there is no database that provides the best performance for all data operations. It is also proved that even though a RDMS stores its data in memory, its overall performance is worse than NoSQL databases. (C) 2016 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

In order to handle the database load for web scale applications, the conventional wisdom is that a cluster of database servers and a caching layer are essential. In this work, we argue that modern main memory database systems are often fast enough to consolidate this complex architecture into a single server (plus an additional fail over system). To demonstrate this claim, we design the Monopedia Benchmark , a benchmark for web scale applications modeled after Wikipedia. Using this benchmark, we show that it is indeed possible to run the database workload of one of the largest web sites in the world on a single database server.

Genome-analysis enables researchers to detect mutations within genomes and deduce their consequences. Researchers need reliable analysis platforms to ensure reproducible and comprehensive analysis results. Database systems provide vital support to implement the required sustainable procedures. Nevertheless, they are not used throughout the complete genome-analysis process, because (1) database systems suffer from high storage overhead for genome data and (2) they introduce overhead during domain-specific analysis. To overcome these limitations, we integrate genome-specific compression into database systems using a specialized database schema. Thus, we can reduce the storage consumption of a database approach by up to 35%. Moreover, we exploit genome-data characteristics during query processing allowing us to analyze real-world data sets up to five times faster than specialized analysis tools and eight times faster than a straightforward database approach.

Computing hardware is constantly evolving and database systems need to adapt to ongoing hardware changes to improve performance. The current hardware trend is heterogeneity, where multiple computing units like CPUs and GPUs are used together in one system. In this paper, we summarize our efforts to use hardware heterogeneity efficiently for query processing. We discuss different approaches of execution and investigate heterogeneous placement in detail by showing, how to automatically determine operator placement decisions according to the given hardware environment and query properties.

The basis of functioning of the systems from different industries are reliable data. They can be both input parameters for operation of individual modules and representation of their work results. Due to multitude of functions performed by these systems, number of necessary parameters is very big. The most convenient form for their storage and processing are database with implemented management system. This article presents how to manage database on example of IT system supporting area of logistics – SIMMAG 3D, prepared within framework of a project funded by the NCBR under the Program for Applied Research (PBS3). The aim of system is modeling of warehouse facilities in 3D, their visualization, spatial planning and evaluation of processes occurring in them, as well as placement of objects in logistics network. SIMMAG 3D system has modular structure and each module gets values from database. The flow of data between its elements was discussed. During flow of data can occur risk of access to them by unauthorized entities. This article analyzes database of SIMMAG 3D system in terms of data security against unauthorized access.

Database performance tuning is a complex and varied active research topic. With enterprise relational database management systems still reliant on the set-based relational concepts that defined early data management products, the disparity between the object-oriented application development model and the object-relational database model, called the object-relational impedance mismatch problem, is addressed by techniques such as object-relational mapping (ORM). This, compounded with changes in the way data is produced, stored and managed can result in generally poor query performance for SQL produced by object-oriented applications and an irregular fit with cost-based optimisation algorithms, and leads to questions about the need for the relational model to better adapt to a more diverse set of queries. This paper discusses existing database performance optimisation techniques and approaches and makes the argument that current database performance tuning approaches need revisiting to support queries developed through ORM tools. This paper also introduces our current research, which includes exploring concepts such as dynamic schema redefinition; query analysis and optimisation modelling driven by machine learning; and augmentation of the cost-based optimiser model.

The use of image processing algorithms for identifying underwater targets does not give results with a high accuracy. This is due to the low visibility of radius of underwater vision, so voice-based signal processing methods should be used. The detection and identification of sound released under the seawater and oceans is very difficult to discern because of the fact that these sound samples are very similar to each other. So, deployment methods based on human perception is difficult and time consuming; hence intelligent and unsupervised methods should be applied. Since there is no standard database for the sounds released under the water, a new database (that was named UWS for underwater database) for this purpose has been introduced. The accuracy of the database performance was evaluated using the Mel-Frequency Cepstral Coefficient (MFCC) method as a feature extraction method and a nonlinear Support Vector Machine (SVM) as the classifier. The results obtained were very promising with an accuracy of 76% being achieved.

The article describes some of the results of database performance studies, identifies features of the influence of some factors on their functioning. The wording of the problem is fulfilled and the ways of their solution are indicated. Also, a model of the element as the main component of the load impacts is proposed. The input and output parameters of the element for load impacts are shown, and their characteristics are provided. An example of load impacts using the software tool for testing open MySQL database connections is given.

Database (DB) stability is a core component of all IT services. Losses from poor performance and unexpected database failures cost millions for companies and take time to recover. In this paper, we introduce a new formalism for finding insight in DB monitoring data, such as performance, query complexity, and events history to increase the ability of the database administrator to respond on different performance issues. A data fusion technique to predict the probability of a critical state of the DB system is proposed. A case study for sets of time series data before DB shutdown is presented. Testing results conclude that the proposed methodology gives better understanding a combination of data, specific behavior of parameters and their contribution to the current problem.

Database tuning is a crucial task to address the performance of information systems that deal with a considerable amount of information stored in databases. Current tuning tools are very platform-specific and do not provide adequate support for the database administrator to reason about performance improvement suggestions. In this paper, we discuss several architectural and implementation decisions of Outer-Tuning, our framework that supports database tuning. Outer-Tuning follows a model-driven development and a modular architecture design, which enabled several benefits. This paper contributes with: (i) the architectural design model adopted in Outer-Tuning, which combines imperative and declarative programming; (ii) the discussions and steps to integrate several software components; and (iii) the actual framework implementation. We assess our framework with an experiment using the TPC-H benchmark. The results evidence that Outer-Tuning infers useful tuning actions and supports the DBA by providing a more semantic environment to create and adapt tuning heuristics using concepts closer to his/her domain, and also relevant information on the rationale of the tuning actions through a friendly web interface.

An efficient selection of indexes is indispensable for database performance. For large problem instances with hundreds of tables, existing approaches are not suitable: They either exhibit prohibitive runtimes or yield far from optimal index configurations by strongly limiting the set of index candidates or not handling index interaction explicitly. We introduce a novel recursive strategy that does not exclude index candidates in advance and effectively accounts for index interaction. Using large real-world workloads, we demonstrate the applicability of our approach. Further, we evaluate our solution end to end with a commercial database system using a reproducible setup. We show that our solutions are near-optimal for small index selection problems. For larger problems, our strategy outperforms state-of-the-art approaches in both scalability and solution quality.