Conventional Database Management Systems Research Articles

RL-instances: An alternative to conjunctive fuzzy sets of tuples for flexible querying in relational databases

In this paper we introduce RL-instances as a way to represent and manipulate gradual conjunctive collections of tuples. Our proposal, based on the theory of Representation by Levels, overcomes the limitations of fuzzy instances in keeping all the properties of conventional Relational Algebra operations. We provide a proof of concept implementation in a conventional relational database management system, where flexible queries can be performed on crisp tables using the same patterns that are used in conventional querying.

Conventional database management systems

Before the advent of Database Management Systems, computer programmers or developers spent a lot of time writing codes to create and manage databases also known as data files before venturing into the actual programming work, thereby making it possible for only trained computer professionals to operate and manipulate databases or data files. It left the programmers who are targeting high performance application to deal with first data integrity and security challenges, inefficient process to access and manipulate stored data, data recovery challenges in the case of crashes and many other challenges. In this paper, we present a review of the traditional database management system (DBMS).First; we gave a detailed explanation of the meaning, types and uses of a database management system. We highlighted the features of Conventional Database Management Systems and the challenges it faces today especially in the handling of high-profile data for many enterprise systems, hardware requirements, human resources, network challenges and costs are also discussed in this research. Finally, we propose a solution to the challenges facing the conventional database system. From our findings, we discovered that the Big Data technology is a potential solution to the limitations of the conventional database management systems. This research will be beneficial to scholars and researchers who are interested in Database management systems.

Big data for better Indian healthcare

Present healthcare breeds an immense amount of data with an exponentially growing rate. This data is not only voluminous but also complex in terms of the different formats because of the diverse sources of the data like Electronic Health Record, clinical data, streaming data from sensors, medical image data, biomedical signal data, laboratory data, etc., which have strained the capabilities of existing conventional relational database management systems. In such a scenario, big data solutions offer to harness these massive, heterogeneous and complex data sets to obtain more meaningful and knowledgeable information. To integrate and find the relationship among these unstructured types of data is a hot topic nowadays. Big data is the hope which can come to rescue in triumphing the insights from these oceanic quantities of data. Big data is a term which is expected to solve the problems of volume, velocity, and variety, mostly present in the healthcare data. This paper presents a new framework for implementing big data solutions in the healthcare sector, the potential opportunities and challenges are also discussed, particularly in the context of Indian healthcare.

A lightweight and efficient temporal database management system in TDSQL

Driven by the recent adoption of temporal expressions into SQL:2011, extensions of temporal support in conventional database management systems (a.b.a. DBMSs) have re-emerged as a research hotspot. In this paper, we present a lightweight yet efficient built-in temporal implementation in Tencent's distributed database management system, namely TDSQL. The novelty of TDSQL's temporal implementation includes: (1) a new temporal data model with the extension of SQL:2011, (2) a built-in temporal implementation with various optimizations, which are also applicable to other DBMSs, and (3) a low-storage-consumption in which only data changes are maintained. For the repeatability purpose, we elaborate the integration of our proposed techniques into MySQL. We conduct extensive experiments on both real-life dataset and synthetic TPC benchmarks by comparing TD-SQL with other temporal databases. The results show that TDSQL is lightweight and efficient.

Big Data technologies to process spatial and attribute data when designing and operating mine-engineering systems

ABSTRACTA myriad of spatial and attribute data are in use when designing and operating mine-engineering systems. These data are hosted on various servers of a company and are diverse in structure of data and units of measurement and have different subject areas related to each other in a complex way. There is no conformity between conventional systems for managing and storing data and tools for analysing incoming data. Big Data paradigm that implies methods for processing distributed data is utilised in ‘big data’ processing. A MapReduce method involves two procedures: map procedure that applies a proper function to each element of the list and reduce procedure, integrating the map procedure results. Such conventional database management system methods as integration and indexation, graph search and other methods are used to cluster big data. These methods should be used within MapReduce. Employing standard equipment and means to control a distributed Hadoop and Apache Hadoop Distributed File System (HDFS) file system, data storages in petabytes can be implemented. To make a more in-depth analysis, Data Mining methods are utilised along with network analysis, predictive analytics, etc. The articlecovers various methods applied to collect and analyse ‘big data’ to do a feasibility study and design mine and engineering systems.

Speeding DBLP querying using hadoop and spark

Big data is becoming bigger every day. Even for simple applications such as the Digital Bibliography & Library Project (DBLP) database, the data is becoming unmanageable using the conventional databases because of its size. Applying big data processing methods such as Hadoop and Spark is becoming more popular because of that. In this work, we investigate the use of Hadoop and Spark in the querying process of big data and we compare the performance of them in terms of their execution time. We use the DBLP database as a case study. Results show that Hadoop and Spark enhances the query execution time significantly when compared with conventional database management systems. We also found that Spark enhances the execution time over Hadoop.

A Survey on Reducing Costs Vs Improving Outcomes and Opportunities in Health Care Field Using Big Data

In recent years, the total amount of medical data collected has grown exponentially, mainly because of the introduction of new medical technologies and the increased use of IT to manage medical health records of patients. These technologies have been introduced not only to provide higher quality of care but also to help solve some of the major healthcare problems in developed countries: the lack of productivity and efficiency, the increasing costs and the relatively low accessibility of the system. Big Data, or the capability to develop, analyze and visualize huge and complex data sets for various research interests and applications, has the promise to radically impact the speed and direction in tackling these problems. Health care field grows tremendously in last few decades. The health care field has generated huge amounts of data that has huge volume, enormous velocity and vast variety. Also it comes from a variety of new sources as hospitals are now tend to implemented electronic health record (EHR) systems. These sources have strained the existing capabilities of existing conventional relational database management systems. In such scenario, Big Data solutions offer to harness these massive, heterogeneous and complex data sets to obtain more meaningful and knowledgeable information. This paper basically studies the impact of implementing the big data solutions on the healthcare field, the potential opportunities, challenges and available platform and tools to implement Big data analytics in health care field.

An Efficient Approach for Modeling Temporal Database with Interval-Based Timestamping in Conventional Database Management Systems

Attaching Date and Time to varying data plays a definite role in representing a dynamic domain and resources on the database systems. The conventional database stores current data and can only represent the knowledge in static sense. Time-varying data representations in relational framework are well described in literature. In this paper we propose an approach for modeling First Normal Form (1NF) and interval-based temporal database over conventional Database Management Systems (DBMS). We choose 1NF approach with interval-based temporal data model for the easily implementation in relational framework as well as to provide the proposed temporal data representation with the modeling and querying power of 1NF relational data model.

A Mathematical Model for Comparing Memory Storage of Three Interval-Based Parametric Temporal Database Models

Interval-Based Parametric Temporal Database Model (IBPTDM) captures the historical changes of database object in single tuple. Such data model violates 1NF and it is difficult to be implemented on top of conventional Database Management Systems (DBMS). The reason behind that, IBPTDM cannot directly use relational storage structure or query evaluation technique that depends on atomic attribute values as well as it is unfixed attribute size. 1NF model with its features can be used to solve such challenge. Modeling time-varying data in 1NF model raise a question about memory storage efficiency and ease of use. A novel approach for representing temporal data in 1NF model and compare it with other main approaches in literature is the main goal of this research. To this end, a mathematical model for comparing a three different storage models is demonstrated to illustrate that the proposed model is more efficient than other approaches under certain conditions. The simulation results showed that the proposed model overcomes the needless redundancy of data, achieves saving in memory storage, and it is easy to be implemented in relational data model or to be adapted with a production systems that need to track temporal aspects of functioning database Systems.

Data Stream of Wireless Sensor Networks Based on Deep Learning

The sensor data in wireless sensor networks are continuously arriving in multiple, rapid, time varying, possibly unpredictable, unbounded streams, and no record of historical information is kept. These limitations make conventional Database Management Systems and their evolution unsuitable for streams. Thereby there is a need to build a complete Data Streaming Management System (DSMS), which could process streams and perform dynamic continuous query processing. In this paper, a framework for Adaptive Distributed Data Streaming Management System (ADDSMS) is presented, which operates as streams control interface between arrays of distributed data stream sources and end-user clients who access and analyze these streams. Simulation results show that the proposed method can thus improve overall system performance substantially.

Big Data Analytics with Apache Hadoop MapReduce Framework

Huge amount of data cannot be handled by conventional database management system. For storing, processing and accessing massive volume of data, which is possible with help of Big data. In this paper we discussed the Hadoop Distributed File System and MapReduce architecture for storing and retrieving information from massive volume of datasets. In this paper we proposed a WordCount application of MapReduce object oriented programming paradigm. It divides input file into splits or tokens that is done with help of java.util.StingTokenizer class. Output file is represented in the form of <, value>. The experimental results are conducted on Hadoop framework by loading large number of input files and evaluating the performance of Hadoop framework with respect to MapReduce object oriented programming paradigm. In this paper we have examined the performance of the map task and the reduce task by loading more number of files and read-write operations that are achieved by these jobs. Keywords: Hadoop, HDFS, Job Tracker, MapReduce, NameNode

Development of Query Transformation Method by Cost Optimization

The transformation time among queries in the database management system (DBMS) is responsible for the execution time of users’ queries, because a conventional DBMS does not consider the transformation cost when queries are transformed for execution. To reduce the transformation time (cost reduction) during execution, we propose an optimal query transformation method by exploring queries from a cost-based point of view. This cost-based point of view means considering the cost whenever queries are transformed for execution. Toward that end, we explore and compare set off heuristic, linear, and exhaustive costbased transformations. Further, we describe practical methods of cost-based transformation integration and some query transformation problems. Our results show that, some cost-based transformations significantly improve query execution time. For instance, linear and heuristic transformed queries work 43% and 74% better than exhaustive queries.

Post Boost Track Processing Using Conventional DBMS Software

&lt;p&gt;The design of air defence, traditional command control system is very challenging which has been used with basic methodologies. Traditional design is associated with unstructured and uncorrelated data and requires huge lines of code using hard disk drive (HDD) in the system. Hence an attempt was made for a better simplified database management system (DBMS) software data access methodology, which processed the incoming airborne data, message in RDBMS database to achieve full automation on real-time. The transaction is accomplished through SQL pass through method from the host decision making system into database. An algorithm of track identification during midcourse track separation was undertaken for prototype development on DBMS data access methodology. In this methodology Oracle C++ calls interface embedded query call was used from the host interface system. The purpose of this development was to find a comparison of online process timing between HDD and SSD using commercial database, and to evaluate performance of dynamic processing of RDBMS Database for identification of target vehicle and booster after separation. Produced experimentation results from improved performance of the proposed methodology on which futuristic command control system can rely.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;

Semantic Retrieval of Relevant Sources for Large Scale Virtual Documents

Abstract The term big data has come into use in recent years. It is used to refer to the ever-increasing amount of data that organizations are storing, processing and analyzing. An Interesting fact with bigdata is that it differ in Volume, Variety, Velocity characteristics which makes it difficult to process using the conventional Database Management System. Hence there is a need of schema less Management Systems even this will never be complete solution to bigdata analysis since the processing has no focus on the semantic information as they consider only the structural information. Content Management System like Wikipedia stores and links huge amount of documents and files. There is lack of semantic linking and analysis in such systems even though this kind of CMS uses clusters and distributed framework for storing big data. The retrieved references for a particular article are random and enormous. In order to reduce the number of references for a selected content there is a need for semantic matching. In this paper we propose framework which make use of the distributed parallel processing capability of Hadoop Distributed File System (HDFS) to perform semantic analysis over the volume of documents (bigdata) to find the best matched source document from the collection source documents for the same virtual document.

The World as Database: On the Relation of Software Development, Query Methods, and Interpretative Independence

In the early 1970s, conventional database management systems were challenged by relational database models. Relational search procedures, as proposed by Edgar F. Codd, offered both improved data independence and an enhanced combinatorial freedom for the user. At the same time, late modern concepts of an open text boosted a reader's interpretative independence, bringing to an end the heyday of hermeneutics. This politically significant change of technocultural patterns led to wild debates, and not only among intellectuals. Likewise, the change meant big trouble for software engineers. Advocates of user independence tried to promote their concepts, destabilize adversative suggestions, and gain allies by aggressive claims, heroic narratives, and cooperative publication strategies.

Transaction processing in a peer to peer database network

This paper investigates a transaction processing mechanism in a peer to peer database network. A peer to peer database network is a collection of autonomous data sources, called peers, where each peer augments a conventional database management system with an inter-operability layer (i.e. mappings) for sharing data. In this network, each peer independently manages its database and executes queries as well as updates over the related data in other peers. In this paper, we consider a peer to peer database network where mappings between peers are established through data-level mappings for sharing data and resolving data heterogeneity. With regards to transaction processing in a peer to peer database network, we mainly focus on how to maintain a consistent execution view of concurrent transactions in peers without a global transaction coordinator. Since there is no global transaction coordinator and each peer executes concurrent transactions independently, different peers may produce different execution views for the same set of transactions. For this purpose, we investigate potential problems that arise when maintaining a consistent execution of concurrent transactions. In order to guarantee consistent execution, we introduce a correctness criteria and propose two approaches, namely Merged Transactions and OTM based propagation. We assume that one single peer initiates the concurrent transactions. We also present a solution for ensuring the consistent execution view of concurrent transactions considering the failures of transactions.

Cloud databases

The panelists will discuss what characterizes data management in the cloud, and how this differs from the broad range of applications that conventional database management systems have supported over the past few decades. They will examine whether we need to develop new technologies to address demonstrably new challenges, or whether we can largely re-position existing systems and approaches. The discussion will cover data analysis in the cloud using Map-Reduce based systems such as Hadoop, and cloud data serving (and so-called "No SQL" systems).

An object placement advisor for DB2 using solid state storage

Solid state disks (SSDs) provide much faster random access to data compared to conventional hard disk drives. Therefore, the response time of a database engine could be improved by moving the objects that are frequently accessed in a random fashion to the SSD. Considering the price and limited storage capacity of solid state disks, the database administrator needs to determine which objects (tables, indexes, materialized views, etc.), if placed on the SSD, would most improve the performance of the system. In this paper we propose a tool called "Object Placement Advisor" for making a wise decision for the object placement problem. By collecting profile inputs from workload runs, the advisor utility provides a list of objects to be placed on the SSD by applying heuristics like the greedy knapsack technique or dynamic programming. To show that the proposed approach is effective in conventional database management systems, we have conducted experiments on IBM DB2 with queries and schemas based on the TPC-H and TPC-C benchmarks. The results indicate that using a relatively small amount of SSD storage, the response time of the system can be reduced significantly by considering the recommendation of the advisor.

Tightly-coupled spatial database features in the Odysseus/OpenGIS DBMS for high-performance

Conventional object-relational database management system (ORDBMS) vendors provide extension mechanisms for adding user-defined types and functions to their own DBMSs. Here, the extension mechanisms are implemented using a high-level (typically, SQL-level) interface. We call this mechanism loose-coupling. The advantage of loose-coupling is that it is easy to implement. However, it is not preferable for implementing new data types and operations in large databases when high performance is required. We have earlier proposed the tight-coupling architecture (Whang et al. 2002, 2005) to satisfy this requirement. In tight-coupling, new data types and operations are integrated into the core of the DBMS engine in the extensible type layer. Thus, they are supported in a consistent manner with high performance. This tight-coupling architecture is being used to incorporate information retrieval features and spatial database features into the Odysseus ORDBMS that has been under development at KAIST/AITrc for 19 years. In this paper, we introduce the tightly-coupled spatial database features of Odysseus/OpenGIS. By taking advantage of tight-coupling, Odysseus/OpenGIS provides excellent performance in processing spatial queries as well as flexible concurrency control and recovery on spatial data. We show the performance through extensive experiments. Finally, we present sample applications of a geographical information system (GIS) implemented using Odysseus/OpenGIS.

Hypertext Design Issues

Hypertext is a computer technology for “nonsequential reading and writing” in which chunks or nodes of information are connected by fixed links. Users access hypertext by selecting cross-reference (or “buttons”) in the context of a given node that is connected via links to other nodes. Such an “associative” strategy for information storage and retrieval differs from the indexing and searching technology of conventional database management and text retrieval systems, and offers significant advantages when used instead of or in combination with search systems. Hypertext is an ideal medium for making job aids, reference materials, and ‘just in time training’ available online to information-based workers. This article reviews five commercially available hypertext systems, and discusses several key hypertext design issues. Among the most important of these issues are the critical role of navigational aids in preventing users from becoming “lost” in the hypertext, the application of structured writing techniques to hypertext development, and the potential contributions of the human performance technology approach to hypertext in the form of systematic analysis, design, and evaluation methods.