Current Database Management Systems Research Articles

Implementasi Sistem Basis Data pada Sektor Pendidikan di Indonesia

DBMS (Database Management System) is a software system that enables database users to maintain, control and access data in a practical and efficient manner. This DBMS is the layer that connects the database with application programs to ensure that the database remains consistently organized and can be accessed easily. The main purpose of using a DBMS in the education sector is to avoid confusion in terms of processing large amounts of data and to help students process data. DBMS also reduces and eliminates data redundancy and maximizes data consistency so that every time it displays data, the data is in accordance with the original. The main function of the DBMS is data storage, due to the complexity of the current DBMS, various types of data can be stored. The results of this study are that database management systems are urgently needed in the education sector so that they can progress and be able to compete, can help process student data, and can be used to create school profiles.

Self-Optimizing Database Architecture

Abstract: Over the most recent decades, analysts and database service providers have fabricated devices to help DBAs (Database Administrators) in various parts of framework tuning and the actual design of the database. Most of this past work, regardless, is fragmented on the grounds that it expects people to come up with an official agreement or judgement about any modifications to the data in the database and fix issues after they happen rather than preventing such cases from taking place or adjusting to these changes automatically. What is required for a really "self-driving" database management system (DBMS) is another way of approaching this that is intended for independent activity and automatic decision making. This is different from prior endeavors since all angles of this framework are constrained by a coordinated arranging part that not just enhance the framework for the current responsibility, but in addition to this, it also predicts future responsibility that might take place and prepares itself for such not-so-common occurrences and adjusts to them as required while keeping the efficiency of the operations as close to normal as possible. With this, the DBMS can uphold all the past tuning procedures without requiring a human to decide the right way and proper opportunity to use them. It likewise empowers new advancements that are significant for current DBMSs (Database Management System), which are impractical today because of the fact that the intricacy of overseeing these frameworks has outperformed the abilities of human specialists who are supposed to tune them and make changes when required. Keywords: Database Management System, Database Administrator, Forecasting, Long Short-Term Memory, Recurrent Neural Networks

Optimization and Upgrade of Special Database Management System for University Archives Based on Cloud Computing

With the progress of society and the development of science and technology, the data management of archives in colleges and universities has also changed from traditional manual operation to information technology system operation, which has taken a big step forward in the speed of data archive management. At the same time, following in the footsteps of the party and the state, according to the education reform measures issued by them, further implementation and implementation. With the innovation of education model, the reform of management method, and the expansion of teaching scale, the types and quantity of files are also increasing, and the scope is also expanding, which leads to the continuous increase of management difficulty. This requires data management to achieve informatization to meet the needs of society. The purpose of this paper is to study the optimization and upgrading of the special database management system of the archives of colleges and universities based on cloud computing. Based on cloud computing, this paper proposes the establishment of a special database management system for college archives and the methods and key technologies used in the process of optimization and upgrading. First, analyze the characteristics of the database management system, and then conduct demand analysis and functional analysis of the system. Secondly, research the advantages and disadvantages between the traditional data management of colleges and universities and the current latest database management system. Finally, according to the needs of the special database and system function requirements, the overall design of the archives management system is carried out, database modeling tools are used to optimize the database, and then its operation is studied, and system testing is carried out to enable it to meet the needs of university archives management. The experimental results show that the focus of database function optimization in different universities is different. University A prefers timeliness, accounting for 46%. University B thinks security is the most important, accounting for as high as 51%. University C is relatively even, both at 30%. The conclusion is to select the most practical and demanding functions for further optimization, and choose an overall design suitable for the special data of colleges and universities.

TransJoin: An Algorithm to Implement Division Operator of Relational Algebra in Structured Query Language

Division operator is one of operators in Relational Algebra which is not implemented directly in SQL (Structured Query Language) standard. Therefore it is not be able to retrieve division query results with an SQL statement in the current relational database management systems (RDBMS). Database programmers have to create a complicated query to perform the task. It is the only relational algebra operators which is not implemented in ANSI SQL standard. This research aimed to study and design an algorithm named TransJoin (transformation and join) to implement it in SQL. TransJoin works to relation P(x, y) divided by Q(y) resulted R(x) through grouping and transforming each y attribute becomes P’(x, y’) and Q’(y’) and each x value is a single tuple with y’ is a composite value of y. Then, result R(x) is resulted of joining P’(x, y’) and Q’(y’) relations by matching y’ attribute. TransJoin was implemented in an open source RDBMS SQLLite. TransJoin tested and delivered valid results by comparing of traditional SQL queries and our proposed SQL queries with various data. The research showed that our algorithm is much more efficient by consuming 0.078 milliseconds compared contrasted the traditional query in 1,974.08 milliseconds for 9,991 tuples.

JagRover

The case is about the dilemma faced by Mr. Keith Gregor, MD of JagRover Warehouse Operations when he realizes that the technology which they are using is soon going to get obsolete. Currently, the warehouse operation is supported by SAP Warehouse Management System which is going to get obsolete in few years according to the product roadmap of SAP. SAP has introduced a new product known as the extended warehouse management system which is embedded in their suite for High Performance Analytic Appliance (HANA) product. This would mean changes for the warehouse operations, not only in terms of the product but also changing their current database management system. These changes will have huge cost implications. Mr. Keith Gregor is under the dilemma whether to invest on the new product or look for some other options. Cost and analytical abilities are the two heads which will majorly drive his investment decision.

Deepening Information Mining Based on Big Data and Refined Decision Management

With the rapid spread of cloud computing technology and the large-scale outbreak of Internet of Things and mobile Internet applications, humanity has entered the era of big data. Big data sets far exceed the capabilities of current typical database management systems to acquire, store, manage, and analyze. The era of big data has characterized by the in-depth excavation of data, prompting the management model to be refined. Although the data mining technology in the era of big data has brought good news for modern management, it also brings with it various challenges such as data security, data integration, and lack of innovative talents. To meet the changes in the era of big data and promote the refinement of management models, we need to pay attention to the combination of three aspects. The need for data mining technology has combined with the reality of our country, the “real-time” of data mining and the “predictive” of the future Integration; the development of data-depth mining technology combined with relevant legal safeguards.

NoSQL real-time database performance comparison

The amount of data being produced is increasing constantly, as the number and variety of connected devices are growing and the advances in data storage and mining are supporting this evolution. However, storing and handling high quantities of data is challenging the current Relational Database Management Systems. Big Data and its related products came to help in this matter, and the NoSQL databases arise with the purpose to offer better solutions and features to handle massive amounts of data with higher performance, sometimes near real-time. The present study presents the NoSQL databases scenario and background, and elaborates a detailed study with the characteristics, a features comparison and a performance evaluation of three different NoSQL databases extensively used in the market nowadays: Couchbase, MongoDB and RethinkDB. Tests were performed in two different scenarios: single thread and multiple threads. The results reveal that Couchbase had a better performance at most of the operations, except for retrieving multiple documents and inserting documents with multiple threads, operations in which MongoDB scored better.

Efficient Set-Correlation Operator Inside Databases

Large scale of short text records are now prevalent, such as news highlights, scientific paper citations, and posted messages in a discussion forum, and are often stored as set records in hidden-Web databases. Many interesting information retrieval tasks are correspondingly raised on the correlation query over these short text records, such as finding hot topics over news highlights and searching related scientific papers on a certain topic. However, current relational database management systems (RDBMS) do not directly provide support on set correlation query. Thus, in this paper, we address both the effectiveness and the efficiency issues of set correlation query over set records in databases. First, we present a framework of set correlation query inside databases. To the best of our knowledge, only the Pearson’s correlation can be implemented to construct token correlations by using RDBMS facilities. Thereby, we propose a novel correlation coefficient to extend Pearson’s correlation, and provide a pure-SQL implementation inside databases. We further propose optimal strategies to set up correlation filtering threshold, which can greatly reduce the query time. Our theoretical analysis proves that with a proper setting of filtering threshold, we can improve the query efficiency with a little effectiveness loss. Finally, we conduct extensive experiments to show the effectiveness and the efficiency of proposed correlation query and optimization strategies.

ScaDiPaSi: An Effective Scalable and Distributable MapReduce-Based Method to Find Patient Similarity on Huge Healthcare Networks

Healthcare network information growth follows an exponential pattern, and current database management systems cannot adequately manage this huge amount of data. It is necessary to use a “big data” solution for healthcare problems. One of the most important problems in healthcare is finding Patient Similarity (PaSi). Current methods for finding PaSi are not adaptive and do not support all data sources, nor can they fulfill user requirements for a query tool. In this paper, we propose a scalable and distributable method to solve PaSi problems over MapReduce architecture. ScaDiPaSi, supports storage and retrieval of all kinds of data sources in a timely manner. The dynamic nature of the proposed method helps users to define conditions on all entered fields. Our evaluation shows that we can use this method with high confidence and low execution time.

Staring into the abyss

Computer architectures are moving towards an era dominated by many-core machines with dozens or even hundreds of cores on a single chip. This unprecedented level of on-chip parallelism introduces a new dimension to scalability that current database management systems (DBMSs) were not designed for. In particular, as the number of cores increases, the problem of concurrency control becomes extremely challenging. With hundreds of threads running in parallel, the complexity of coordinating competing accesses to data will likely diminish the gains from increased core counts. To better understand just how unprepared current DBMSs are for future CPU architectures, we performed an evaluation of concurrency control for on-line transaction processing (OLTP) workloads on many-core chips. We implemented seven concurrency control algorithms on a main-memory DBMS and using computer simulations scaled our system to 1024 cores. Our analysis shows that all algorithms fail to scale to this magnitude but for different reasons. In each case, we identify fundamental bottlenecks that are independent of the particular database implementation and argue that even state-of-the-art DBMSs suffer from these limitations. We conclude that rather than pursuing incremental solutions, many-core chips may require a completely redesigned DBMS architecture that is built from ground up and is tightly coupled with the hardware.

A Framework to Subquery Optimization using Case-based Reasoning

Query optimizers in current database management systems (DBMS) often face problems such as intolerably long optimization time and/or poor optimization results when optimizing complex subqueries using classical techniques [1]. There are computational environments where metadata acquisition and support is very expensive. A ubiquitous computing environment is an appropriate example where classical query optimization techniques are not useful any more. To tackle this challenge, we present a new similaritybased optimization technique using case-based reasoning in this paper[2]. The key idea is to identify cases of similar subqueries that often appear in a complex query and share the optimization result within each case in the query [3]. An efficient algorithm to identify similar queries in a given query and optimize the query based on similarity is presented. Our experimental results demonstrate that the proposed technique is quite promising in optimizing complex subqueries in a DBMS. It is possible to learn from each new experience in order to suggest better solutions to solve future queries.

Operational Business Intelligence: Processing Mixed Workloads

Modern business applications have operational business intelligence features that require processing analytical queries and business transactions together at the same time and on the same data. This results in mixed workloads, which are a big challenge for current database management systems. Cloud computing fosters the development of data management systems optimized for specific application scenarios. The authors propose an architecture for software-as-a-service business applications that helps process these mixed workloads with a special-purpose main-memory database system, while meeting strict service-level objectives with stringent response-time and throughput guarantees.

A Facilitated Interface to Generate a Combined Textual and Graphical Database System Using Widely Available Software

Data-Base Management System (DBMS) is the current standard for storing information. A DBMS organizes and maintains a structure of storage of data. Databases make it possible to store vast amounts of randomly created information and then retrieve items using associative reasoning in search routines. However, design of databases is cumbersome. If one is to use a database primarily to directly input information, each field must be predefined manually, and the fields must be organized to permit coherent data input. This static requirement is problematic and requires that database table(s) be predefined and customized at the outset, a difficult proposition since current DBMS lack a user friendly front end to allow flexible design of the input model. Furthermore, databases are primarily text based, making it difficult to process graphical data. We have developed a general and nonproprietary approach to the problem of input modeling designed to make use of the known informational architecture to map data to a database and then retrieve the original document in freely editable form. We create form templates using ordinary word processing software: Microsoft InfoPath 2007. Each field in the form is given a unique name identifier in order to be distinguished in the database. It is possible to export text based documents created initially in Microsoft Word by placing a colon at the beginning of any desired field location. InfoPath then captures the preceding string and uses it as the label for the field. Each form can be structured in a way to include any combination of both textual and graphical fields. We input data into InfoPath templates. We then submit the data through a web service to populate fields in an SQL database. By appropriate indexing, we can then recall the entire document from the SQL database for editing, with corresponding audit trail. Graphical data is handled no differently than textual data and is embedded in the database itself permitting direct query approaches. This technique makes it possible for general users to benefit from a combined text-graphical database environment with a flexible non-proprietary interface. Consequently, any template can be effortlessly transformed to a database system and easily recovered in a narrative form.

Purpose-based access control exploits by HDB

As organizations increase their reliance on, as a result preserving privacy has become a crucial requirement for operating a business that manages personal data. Private Schools, district schools, and state education agencies maintain a large volume of personnel files. Due to voluminous stores of personal data being held by Education Industry today, preserving privacy has become a crucial requirement for operating a business. Hippocratic databases have been proposed to answer this requirement through a database design that includes responsibility for the privacy of data as a founding tenet. We identify, study, and implement privacypreserving feature for education industry in Hippocratic databases. This paper includes the support of how current relational database management systems can be transformed into their privacy preserving equivalents.

Access Control for Databases: Concepts and Systems

As organizations depend on, possibly distributed, information systems for operational, decisional and strategic activities, they are vulnerable to security breaches leading to data theft and unauthorized disclosures even as they gain productivity and efficiency advantages. Though several techniques, such as encryption and digital signatures, are available to protect data when transmitted across sites, a truly comprehensive approach for data protection must include mechanisms for enforcing access control policies based on data contents, subject qualifications and characteristics, and other relevant contextual information, such as time. It is well understood today that the semantics of data must be taken into account in order to specify effective access control policies. To address such requirements, over the years the database security research community has developed a number of access control techniques and mechanisms that are specific to database systems. In this monograph, we present a comprehensive state of the art about models, systems and approaches proposed for specifying and enforcing access control policies in database management systems. In addition to surveying the foundational work in the area of access control for database systems, we present extensive case studies covering advanced features of current database management systems, such as the support for fine-grained and context-based access control, the support for mandatory access control, and approaches for protecting the data from insider threats. The monograph also covers novel approaches, based on cryptographic techniques, to enforce access control and surveys access control models for object-databases and XML data. For the reader not familiar with basic notions concerning access control and cryptography, we include a tutorial presentation on these notions. Finally, the monograph concludes with a discussion on current challenges for database access control and security, and preliminary approaches addressing some of these challenges.

PIST: An Efficient and Practical Indexing Technique for Historical Spatio-Temporal Point Data

Despite pressing need, current relational database management systems (RDBMS) support for spatio-temporal data is limited and inadequate, and most existing spatio-temporal indices cannot be readily integrated into existing RDBMSs. This paper proposes a practical index for spatio-temporal (PIST) data, an indexing technique, rather than a new indexing structure, for historical spatio-temporal data points that can be fully integrated within existing RDBMSs. PIST separates the spatial and temporal components of the data. For the spatial component, we develop a formal cost model and a partitioning strategy that leads to an optimal space partitioning for uniformly distributed data and an efficient heuristic partitioning for arbitrary data distributions. For the temporal component of the data a B?+?-tree is used. We show that this layer's performance can be maximized if an optimal maximal temporal range is enforced, and we present a procedure to determine such an optimal value. Being fully mapped onto a RDBMS, desirable and important properties, such as concurrency control, are immediately inherited by PIST. Using ORACLE as our implementation platform we perform extensive experiments with both real and synthetic datasets comparing its performance against other RDBMS-based options, as well as the MV3R-tree. PIST outperforms the former by at least one order of magnitude, and is competitive or better with respect to the latter, with the unarguable advantage that it can readily used on top of virtually any existing RDBMS.

Tractable Reasoning and Efficient Query Answering in Description Logics: The DL-Lite Family

We propose a new family of description logics (DLs), called DL-Lite, specifically tailored to capture basic ontology languages, while keeping low complexity of reasoning. Reasoning here means not only computing subsumption between concepts and checking satisfiability of the whole knowledge base, but also answering complex queries (in particular, unions of conjunctive queries) over the instance level (ABox) of the DL knowledge base. We show that, for the DLs of the DL-Lite family, the usual DL reasoning tasks are polynomial in the size of the TBox, and query answering is LogSpace in the size of the ABox (i.e., in data complexity). To the best of our knowledge, this is the first result of polynomial-time data complexity for query answering over DL knowledge bases. Notably our logics allow for a separation between TBox and ABox reasoning during query evaluation: the part of the process requiring TBox reasoning is independent of the ABox, and the part of the process requiring access to the ABox can be carried out by an SQL engine, thus taking advantage of the query optimization strategies provided by current database management systems. Since even slight extensions to the logics of the DL-Lite family make query answering at least NLogSpace in data complexity, thus ruling out the possibility of using on-the-shelf relational technology for query processing, we can conclude that the logics of the DL-Lite family are the maximal DLs supporting efficient query answering over large amounts of instances.

An algebraic framework for temporal attribute characteristics

Most real-world database applications manage temporal data, i.e., data with associated time references that capture a temporal aspect of the data, typically either when the data is valid or when the data is known. Such applications abound in, e.g., the financial, medical, and scientific domains. In contrast to this, current database management systems offer preciously little built-in query language support for temporal data management. This situation persists although an active temporal database research community has demonstrated that application development can be simplified substantially by built-in temporal support. This paper's contribution is motivated by the observation that existing temporal data models and query languages generally make the same rigid assumption about the semantics of the association of data and time, namely that if a subset of the time domain is associated with some data then this implies the association of any further subset with the data. This paper offers a comprehensive, general framework where alternative semantics may co-exist. It supports so-called malleable and atomic temporal associations, in addition to the conventional ones mentioned above, which are termed constant. To demonstrate the utility of the framework, the paper defines a characteristics-enabled temporal algebra, termed CETA, which defines the traditional relational operators in the new framework. This contribution demonstrates that it is possible to provide built-in temporal support while making less rigid assumptions about the data and without jeopardizing the degree of the support. This moves temporal support closer to practical applications.

Managing hospital databases: can large hospitals really protect patient data?

Between 1998 and 2003 a number of European countries, the UK, Canada, Australia and the US all introduced data privacy legislation that sought to comply with the European Data Privacy Directive of 1995 in protecting the privacy of individuals undergoing treatment in large hospitals. In 2004 we find that hospital administrators within these jurisdictions are still struggling to find ways to implement and maintain hospital databases while complying with the given legislation - where compliance seems to require a whole new approach to database management. This research examines the UK Data Protection Act 1998 and considers whether current database management systems allow the EU Directives contained in the Act to be followed in practice. It finds a number of recurrent problems with hospital systems that would make compliance with the Act difficult. These findings have significant implications for hospital information systems development and design.

Semantic errors in SQL queries: A quite complete list

We investigate classes of SQL queries which are syntactically correct, but certainly not intended, no matter for which task the query was written. For instance, queries that are contradictory, i.e. always return the empty set, are obviously not intended. However, current database management systems (DBMS) execute such queries without any warning. In this paper, we give an extensive list of conditions that are strong indications of semantic errors. Of course, questions like the satisfiability are in general undecidable, but a significant subset of SQL queries can actually be checked. We believe that future DBMS will perform such checks and that the generated warnings will help to develop application programs with fewer bugs in less time.