Conventional Database Technology Research Articles

Blockchain-Based Smart Contract for P2P Energy Trading in a Microgrid Environment

The purpose of this study is to propose a method of peer-to-peer (P2P) energy trading that allow prosumers with energy deficiency to buy energy from prosumers with excess energy in a microgrid system. The proposed method solves the problems associated with lack of trust in P2P energy trading and utilized the blockchain technology that made it impossible to tamper with data. The data is referred as transaction generated by using blockchain. A blockchain based smart contract execute the trading and payment rules without the intermediaries. Thus, the security and fairness of energy trading are significantly enhanced compared to conventional database technology. Without the third-party intervention, a miner that is selected among the participants in the microgrid environment process all the transaction generated from buying and selling energy. The smart contract consists of two main components; bidding and settlement module, payment module. The smart contract deployed in blockchain test network to test the interaction of smart contract. From the simulation, the proposed method is validated using realistic data with the Ethereum Virtual Machine (EVM). The method will be expected to be useful to designers who need to integrate renewable energy in a microgrid system.

Big Data Model for Financial Assistance to Students- A Paradigm Shift

Big Data Technologies for managing huge amounts of student data which is rising exponentially year after year specially in the countries like India, where there is a large no of young population is a need not luxury. Many students are searching for courses of their choice and for funding their education interests they look for scholarships. There are many scholarship schemes available for students of different status in the society. Educational institutions look for solutions to handle large amount of student data ranging from enrollments, scholarships, academic content, literature search, testing, results and finally placements. Such data require a lot of integrity and scalability. Memory saving schema for hardware and software platforms for the needy Universities and Colleges along with lot of mobility for their executives to access and analyze such data remain in focus. In this paper a new data base model has been suggested using modern day Big Data technologies rather than using conventional database technologies to handle complex data generated through student activity which can be analyzed to reach decisions. New model suggested after testing real time data sets of scholarships management of technical education system. Lot of memory and cost has been saved along with achieving data integrity, security and improvement in response time.

Machine Learning Based Method for Prediction of Heart Disease in Big Data Environment

Prediction of diseases is one of the challenging tasks in healthcare domain. Conventionally the heart diseases were diagnosed by experienced medical professional and cardiologist with the help of medical and clinical tests. With conventional method even experienced medical professional struggled to predict the disease with sufficient accuracy. In addition, manually analysing and extracting useful knowledge from the archived disease data becomes time consuming as well as infeasible. The advent of machine learning techniques enables the prediction of various diseases in healthcare domain. Machine learning algorithms are trained to learn from the existing historical data and prediction models are being created to predict the unknown raw data. For the past two decades, machine learning techniques are extensively employed for disease prediction. Despite the capability of machine algorithm on learning from huge historical data which is stored in data mart and data warehouses using traditional database technologies such as Oracle OnLine Analytical Processing (OLAP). The conventional database technologies suffer from the limitation that they cannot handle huge data or unstructured data or data that comes with speed. In this context, big data tools and technologies plays a major role in storing and facilitating the processing of huge data. In this paper, an approach is proposed for prediction of heart diseases using Support Vector Algorithm in Spark environment. Support Vector Machine algorithm is basically a binary classifier which classifies both linear and non-linear input data. It transforms the non-linear data into hyper plan with the help of different kernel functions. Spark is a distributed big data processing platform which has a unique feature of keeping and processing a huge data in memory. The proposed approach is tested with a benchmark dataset from UCI repository and results are discussed.

On-Demand ELT Architecture for Right-Time BI

In a typical BI infrastructure, data, extracted from operational data sources, is transformed, cleansed, and loaded into a data warehouse by a periodic ETL process, typically executed on a nightly basis, i.e., a full day’s worth of data is processed and loaded during off-hours. However, it is desirable to have fresher data for business insights at near real-time. To this end, the authors propose to leverage a data warehouse’s capability to directly import raw, unprocessed records and defer the transformation and data cleaning until needed by pending reports. At that time, the database’s own processing mechanisms can be deployed to process the data on-demand. Event-processing capabilities are seamlessly woven into our proposed architecture. Besides outlining an overall architecture, the authors also developed a roadmap for implementing a complete prototype using conventional database technology in the form of hierarchical materialized views.

Design of Hadoop-based Framework for Analytics of Large Synchrophasor Datasets

The power sector is increasingly utilizing GPS-stamped real-time measurements from Phasor Measurement Units (PMU) to improve the reliability and efficiency of power grids. PMUs directly measure phase angles in real-time, which allows operators to perform grid optimization that was not possible in the past. In early 2010, almost 250 PMU's were deployed across North America and it continues to increase remarkably. However, one of the major challenges is the complexity of analyzing such a large amount of real-time datasets. The phasor data from PMU's will be accumulated in peta-bytes in coming years, which exceeds the capability of conventional relational database technologies. A new software and architecture framework is in desperate need to process such a large amount of data in real-time reliably and cost- effectively. The paper presents a new framework based on Hadoop, an open-source system widely used in the industry, to perform distributed and parallel analytics on large synchrophasor datasets. The paper demonstrates various applications of MapReduce to analyze patterns of load distribution using parallel node calculations, which can later be scaled up to match the requirements for power utility sector. The paper serves as a pilot study on data analytics on big data of smart grids.

An Algorithm to Simulate the Temporal Database Using Exponential Distribution

Since the early eighties, an active temporal database research community has sought new insight into the management of time-referenced, or temporal, data and has developed concepts, tools and techniques that better support the management of such data. Much of this activity has been motivated by the observations that most databases contain substantial amounts of temporal data and that conventional database technology offers precious little support for temporal data management. Temporal data stored in a temporal database is different from the data stored in non-temporal database in that a time period attached to the data expresses when it was valid or stored in the database. In this paper we are going to design an algorithm for simulating temporal database using Exponential Distribution.

Dynamic Tables: An Architecture for Managing Evolving, Heterogeneous Biomedical Data in Relational Database Management Systems

Data sparsity and schema evolution issues affecting clinical informatics and bioinformatics communities have led to the adoption of vertical or object-attribute–value-based database schemas to overcome limitations posed when using conventional relational database technology. This paper explores these issues and discusses why biomedical data are difficult to model using conventional relational techniques. The authors propose a solution to these obstacles based on a relational database engine using a sparse, column-store architecture. The authors provide benchmarks comparing the performance of queries and schema-modification operations using three different strategies: (1) the standard conventional relational design; (2) past approaches used by biomedical informatics researchers; and (3) their sparse, column-store architecture. The performance results show that their architecture is a promising technique for storing and processing many types of data that are not handled well by the other two semantic data models.

A Short History of the Last 15 Year's Quest for It Interoperability in the Petroleum E&P Industry

This paper gives a personal view on the drivers of the different actors of the E&P petroleum Industry (companies, vendors, certification organisations) to obtain a good level interoperability between data and applications. This is illustrated by a synthetic view of several interoperability projects dealing with subsurface aspects (geology, geophysics, reservoir). In fact, the oil and gas profession started to address subsurface data interoperability problems back in the 1990's with the well-known POSC consortium, which led to the reference Epicentre model. Since those days, many projects have been undertaken, dealing with all sorts of applications (data exchange, software interoperability, workflow modelling etc.) and using all sorts of technologies, from conventional database technologies to more advanced semantic models on top of XML. This article will help the reader find his/her way among the POSC, Epicentre, RESCUE, OpenSpirit, EpiSEM and WITSML projects for this key application domain which is only at the beginning of its life.

Software tools to support incident reporting in safety-critical systems

Incident reporting systems are playing an increasingly important role in the development and maintenance of safety-critical applications. The perceived success of the FAA's Aviation Safety Reporting System (ASRS) and the FDA's MedWatch has led to the establishment of similar national and international schemes. These enable individuals and groups to report their safety concerns in a confidential or anonymous manner. Unfortunately, many of these systems are becoming victims of their own success. The ASRS and MedWatch have both now received over 500,000 submissions. In consequence, the administrators of incident reporting systems increasingly rely upon software tools to support the administration of their systems. In the past, these systems have relied upon ad hoc applications of conventional database technology. However, there are several reasons why this technology is inadequate for many large-scale reporting schemes. In particular, the problems of query formation often result in poor precision and recall. This, in turn, has profound implications for safety-critical applications. Users may fail to identify similar incidents within national or international collections. These ad hoc approaches also neglect the opportunities provided by recent developments in computer assisted interviewing and in the monitoring of retrieval activities to build models of user behavior. These techniques offer a number of potential benefits. For instance, it is possible to automatically detect potential biases in the way that investigators analyze particular incidents.

Misconceptions about real-time databases

Increasingly, computer systems-even general purpose systems-are requiring real time support, so it's not surprising to hear more about real time databases. Unfortunately, there are many misconceptions about the real time aspects of databases. Mainstream database users who don't have any experience with real time systems mistakenly think they are synonymous with speed. Others argue that conventional database technology can be used in real time applications. To help bring order to the current state of confusion, the authors review nine common misconceptions about real time databases. They also describe key research areas that need to be addressed to encourage increased activity in this challenging area.

Databases for criminal intelligence analysis: Knowledge representation issues

Criminal intelligence data poses problems for conventional database technology. It has little structure or homogeneity and queries may involve looking for unknown associations between entities; such open-ended queries cannot be made in current systems. Finally, the data must be presented in an intuitively simple fashion for both investigative and evidential purposes. We discuss a database system which uses a labelled graph as its data model. This approach obviates the need for schema design, allows queries which look for associations between entities to be implemented and provides the basis for a natural visual representation of the data.

Object-oriented databases for new applications

As applications have grown more sophisticated, they have placed greater demands on underlying database technology. In this paper we examine some of the shortcomings of conventional database technologies in the face of these demands, and then discuss how emerging object-oriented database technology addresses these shortcomings in several specific application areas: user interface applications, multimedia databases, CAD databases, statistical and scientific databases, and management of heterogeneous database systems. We conclude with a list of remaining challenges and some suggestions for further research.

Consistency management in a project management assistant

Object management systems have been identified as the core of object-oriented software development environments. One of the most important objectives of object management systems is to maintain consistency between the vast amount of interrelated objects, which is generated, accessed, and manipulated throughout the software life cycle. Consistency management in such systems is beyond the reach of conventional database technology due to the complex structure and the incompleteness of data, the dynamic nature of constraints, and the need to tolerate various levels of inconsistency. We investigate the design issues for consistency management in the context of a Project Management Assistant. Our consistency manager organizes constraints into a hierarchy of consistency, and associates a spectrum of management techniques with classes of constraints. The various levels of consistency are achieved by enabling or disabling classes of constraints, and propagating constraints cross the class boundary. The constraint manager is capable of compiling constraint specifications into triggers attached to objects in the object base. The architecture is open to new constraints and new management techniques.