The Database Management Process - Term Paper Example

Data Base Management Contents 3 Introduction 3 Analysis 4 Challenges in the management of databases 8 Critical Analysis and Future Predictions 9 Conclusion 10 Reference List 12 Abstract There has been a dramatic change in data processing and management of databases in the past three decades. Most of these changes have been essentially evolutionary. When data was processed with conventional files, it fulfilled the need existing during those eras. With increasing complexity in data as well as enormous volume of data generation, users started to feel the need for data processing, filtering, monitoring and even controlling it. At present, almost every organization needs to access, monitor and control data. Database management is a not a new concept, but its implementation and complexity has changed drastically over years. In the current research essay, three research articles have been chosen for analyzing the database management process, its evolution as well as present application and future implications. The critical findings from the three articles were critically assessed for comprehending the database management process. The reflection and future prediction of the articles as well as other sources was based on self-understanding and critical analysis of the researcher. At the end of this research essay, future prospects and further research queries have also been presented. Introduction Database management is a process, where multiple data are stored in a definitive database. A database management system is more than just a set for storing different data or files (Date, 1995). These data can be of different types related to application data or meta-data used for defining database schema or database layout. For instance, in case of relational database management (RDBMs), relationship between multiple data is defined and stored in the form of tables. These can be accessed through relational views or logical queries (Yang, 2003). With the help of database management system, processing has become extremely efficient and useful. Data independence is achieved within software chain by analyzing location of the management system. One of the major advantages of having a database management system is that the user is able to store data in one particular location and simultaneously access it from numerous other locations. This data can also be used by different departments and systems and unplanned redundancy is avoided. Data integrity within the management system is obtained by a software mask. Organizations are able to achieve economies of scale by installing central database manager and by supporting it with trained professional groups. At the same time, there are some downsides of installing and using database management. Firstly, it is not free. Organizations have to spend huge amount in selecting, installing as well as maintaining databases. Management of database is a tricky task and the systems associated are complex. As a result, organizations need to hire trained professionals and experts in order to successfully install, implement, manage the database management system as well as train internal employees. Also, there are costs associated with adjustments among employees, development of the software as well as organizational change (Yang, 2003). Most of the research and critical reviews conducted in this field have focused on the changes and developments made in database management. Some of the major research papers evaluated for the current research essays have stressed upon the emerging themes as well as future requirements in the field of database management. However, the major limitation of these researches is real life examples, such as, case studies or examples, where companies have successfully implemented or altered their database management, thereby enhancing overall operational strategies. In this research studies, few important examples have been illustrated helping the reader to understand the meaningful application of database management in dissimilar and unique situations (Date, 1995). The overall objective of the research paper is to investigate chief emerging themes surrounding database management. Within this overall objective, the specific issues regarding database management have been covered through research on articles, journals and numerous critic issues. Surveys and research conducted on database management across various multidisciplines have also been examined. The paper has considered the advancement in DBM technologies as well as its monitoring and control techniques. Previous surveys on the sub-disciplines of database management have been studied. The findings from previous research topics were critically evaluated and at the end, some open and critical research questions have been presented that can be further researched. Analysis In order to clearly understand the critical importance of database management in present environment, it is essential to evaluate its origin and evolution over years. A research paper was presented by Gray (1996) from Microsoft Research, where the author presented an analytic research brief on the evolution of database management, its present state, applications in various fields as well as future implications. Management of database can be categorized into six distinctive phases. Initial processing of data was completely manual. In the next stage, electro-mechanical machines and punched-card equipments were used for sorting and tabulating records in millions. During the third phase, data was stored on program computers and magnetic tapes and was used for performing batch processing (Gray, 1996). The first phase ran from 4000 BC to 1900 and saw many technological evolutions such as, phonetic alphabets, ledgers, libraries and printing press. In the second phase, which existed from 1900 to 1955, automatic information processing systems were introduced. For instance, in order to perform US census, punched-card technology was implemented. In the third generation, which was from 1955 to 1970, equipments or computers for recording programmed units were developed (Gray, 1996). These computers had the power of processing hundreds of data and records every second. The fourth phase of the evolution of database management was from 1965 to 1980. This phase saw the evolution of concepts such as, online navigations and database schemas, which were incorporated for accessing data. During this time, teleprocessing monitors helped in accessing specialized software for multiplying numerous terminals. By 1980, hierarchal models of data and set-oriented networks became very popular. Bachman founded a company called Cullinet, which became the fastest and largest growing software firm during that time. The fifth phase of database evolution was from 1980-1995 (Gray, 1996). During this phase, relational databases were automatically automated and new features such as, client-server and distributed processing, were added. Even though the network model was hugely successful, many designers felt that programming navigation was of low-level and could not serve the purpose of efficiency and speed at the same time. Software professionals found these databases difficult to program and design (Yang, 2003). According to a paper outlined by Cody in 1970, relational model of database management provided a superior alternative to the current navigational interfaces. This relational model helped in representing both relationships and entities in uniform manner. This data model has a unique language for data navigation, data definition as well as data manipulation. Apart from that, management tasks are conducted in lesser time by using the relational model for database management. The sixth phase of database management started after 1995 and is presently running across all programs (Gray, 1996). This is the most advanced evolutionary phase of database management where richer data such as, images, video data, documents and voice, are stored. The current database management systems act as a storage engine for emerging intranet and internet. The major obstacles during the fifth phase were observed by communities coming from object-oriented programming. Unification of data and procedures and an effective data model was missing from data design available during the fourth generation (Gray, 1996). While the relational system from fifth generation offered big improvements in terms of graphical interface ease-of-use, distributed database, data mining, data search and client-server applications, users as well as research communities started to seek a more technology advanced model. In the traditional model, data and programs were clearly separated. This model was beneficial when data available was in the form of characters, lists, numbers, arrays or record sets. However, with emergence of new applications, problems occurred while separating data and programs. For instance, in case of complex data systems, methods for searching, comparing as well as manipulation was peculiar and unique to specific image, map data type, sound or image. In the traditional SQL model, new data types, two-byte strings of characters and time intervals were added. Each was significantly different from other. Nonetheless, appropriate results could not be achieved after completion of the model. In order to resolve the above issues, professionals started to revolutionize the present SQL system (Yang, 2003). A class library was added, comprising methods for creating, updating as well as deleting time series. This helped in summarizing the trends, differentiating and combining two series as well as interpolation of the points in the time series. After establishment of the modern class library, users were able to plug the library into any system. The database not only stores objects of its own type, but also manages concurrency, securing, indexing and recovery of the data. The present database management system also manages behaviour and content of the time-series objects (Gray, 1996). Another research paper on database management, which has been critically analyzed for the current research, was based on emerging multidisciplinary topic areas in database management. Nica, Suchanek and Varde (2007) conducted the research based on critical subsystems, which are continuously working within database management across various fields. The relevance of this research is that it allows a quick insight into the integral components of a database management and offers wide array of application based recommendations. The research paper surveyed multiple database management topics, including data stream, ontology development, processing of natural language, green energy, medical databases, exploratory search and green energy. Data mining is a process of extracting meaningful data. At present, data mining faces many issues in terms of alignment and integration of tasks. Three fundamental problems identified during data mining process were efficient indexing and graph storage, locating sub-graph isomorphism through SQL and frequent sub-graph search. The analysis of the research paper suggested Semantic Web as an appropriate alternative for mining data. It is useful during alignment and summarization and can be extremely beneficial while filtering data. Another area recognized was information retrieval process, which has become complicated due to establishment of multiple storage systems in the environment. Critical analysis of the article reveals that this issue can be solved by creating a distributed environment (Nica, Suchanek and Varde, 2007). As data is becoming more powerful as well as larger, a major problem that is occurring in the field of database management is its compatibility with the environment and management of the heat. With the objective of minimizing negative impact of this database management software, various decisions can be taken on aspects such as, acoustic levels, humidity and data on usage of energy as well as carbon footprint produced by multiple centers harbouring database management systems (Nica, Suchanek and Varde, 2007). Jagadish and Olken (2004) wrote a compelling article on the application of database management in the subjects of life science such as, molecular biology. The research article emphasized on the increasing need of effective database management systems and programs in the field of life sciences, owing to increasing complexity while handling data. Even fifteen years ago, life science was a cottage industry, characterized by expensive and scarce data that could be majorly obtained manually by small groups constituting post-graduate doctors, graduate students and few skilled technicians. Contrarily, this industry has become highly mechanized as well as data rich and now houses advanced organizations producing factory scale data. As such, it has become imperative that the life science industry is equipped with appropriate database management, monitoring and control systems with extensive automation for both sample preparation and sequencing (Jagadish and Olken, 2004). Their research revealed significant benefits of implementing database management in monitoring and controlling the data compared to their manual segregation and control. With availability of a management tool for data, it is faster to search heavy quantity genomic sequencing and similar databases, which in turn enhances overall utility of these data sequences to a great extent. In a similar manner, experts have also argued that many data instruments that are used in chemistry and molecular biology, such as, infrared spectrometers, liquid and gas chromatography as well as mass spectrometers, help in quick analysis of data. Even so, their usage can be substantially enhanced by complementing them with bigger community spectra databases and effective database management models. As a result, data can be easily stored as well as retrieved, while offering higher value to biochemistry, biology, medicine and forensics (Jagadish and Olken, 2004). A critical finding from the above article was the application of database management for various biological data. For instance, biological data consists of multiple graphs such as, undirected or directed labelled graphs, hyper graphs and nested graphs, which can be stored as well as extracted effortlessly from database management models. Similarly, other types of complicated data such as, high-dimensional data, temporal data, shapes, patterns, vector and scalar fields, are easily stored in relational databases, so that they can be segregated, filtered and used whenever required (Jagadish and Olken, 2004). Challenges in the management of databases A heated debate still exists on the revolution versus evolution outcome of database management models. No debate has arisen so far on retrieving and storing objective, which class libraries manage. The major debate revolves around SQL’s role, object model details and class libraries supporting the database management system. Rapid evolution of the Internet has amplified the debate and consequently, the critical distinction between web and database has been blurred. In such a competitive environment, vendors are promising universal servers that can analyze and store all data forms. The current worldwide library is another example where the database is facing challenges. Majority of institutional libraries have online holdings. Scientific literatures are also being increasingly published online. Considering online publishing, numerous societal issues are arising such as, infringement of intellectual property and copyright issues. Apart from that, online publishing has also entailed critical technical challenges. Huge amounts of information are being generated every day and it is extremely difficult to manage such diversified data. Also, the information mainly appears in multiple languages and data formats. Critical Analysis and Future Predictions Computer hardware advancements have transformed database management evolution to high-end technology based modern information based search engines, from paper-based processing systems. This advancement has succeeded those made in hardware. The set oriented and record systems, which produced relational systems, have evolved to unique object-relational systems. One unique application of this database management is during development of prototypes into products. Industrial and academic research labs gave rise to parallel, active, relational and object-relational database. With easy software and inexpensive hardware, computers are nowadays accessible by every individual and creating a database or a web server is relatively easier. It can be said that the never-ending demand of users have resulted in more complexity in database management systems. Users are now expecting databases to automatically manage and design themselves. These users also expect new applications without any effort as well as intuitive and unique graphical interface required for all operations, design and administration tasks. Majority of these challenges are both societal and technical. For instance, a major issue arising from bigger databases is privacy and copyright dilemmas (Zhang, 2001). Users give in a lot of information through various sources such as, in social media sites, during purchasing, filling surveys and even during browsing. This information can be utilized by various marketers for launching multiple offers according to user’s demands and purchase trends. Many times, this information is misplaced, hacked or even sold to vested parties, creating privacy infringement of user’s information. With increasing online transactions, as more and more data is fed into the database management systems, security of confidential data is a big doubt. In a recent self-assessment study, critics have identified major challenges pertaining to managing databases in future. The first will be defining data models and their integration with traditional management systems. The next challenge is to scale the huge databases in appropriate space, diversity and size. Following that is the issue of automatic discovery of patterns, data trends, atomization of database administration and design as well as anomalies during data analysis and data mining. Collaboration and integration of data from more than one source with similar speed and efficiency can also be inconvenient in future (Zhang, 2001). Similarly, a critical analysis of the article on application of database management in sectors, such as, life sciences, has revealed few compelling facts. Even though relational databases are used for storing and managing biological data such as, graphs, they are still not adequately advanced in order to store or analyze complicated sub-graphs such as, homomorphism, isomorphism and homeomorphism. As a result of high complexity of data, researchers are also unable to easily segregate one data from another, majorly due to their smaller composition, high variability as well as high volatility. Data provenance or the system for processing original data history is necessary for effective sharing of biomedical and biological data. However, issues related to data provenance have been mostly neglected, which needs to be further studied for supporting and sharing of bioinformatics data. Conclusion The objective of the current research article was to present a critical analysis database management. For this, the major study materials were three published research papers, where concepts and applications of database management were evaluated. An initial introduction and understanding of the topic revealed that database management has become an integral part of every organization, regardless of being industrial, commercial or related to life science. Also, the analysis revealed some interesting facts regarding the evolution of database management such as, transformation of database to relational and object-oriented database systems. A research on the past, present as well as future prospects of database management pointed out the changing paradigm in this area. Furthermore, the study conducted on various sub-groups and related areas of database management were helpful in analyzing the various trends prevailing in database market. In addition, research on application of database management in life science suggested that the present relational databases are not sufficient to cater to every need of the life science industry and the research community will have to establish more advanced and efficient systems for managing databases. Reference List Date, C. J., 1995. An Introduction to Database Systems. London: Addison Wesley. Gray, J., 1996. Data Management: Past, Present, and Future. IEEE Computer, 29(10), pp. 38-46. Jagadish, H.V. and Olken, F., 2004. Database Management for Life Sciences Research. SIGMOD Record, 33(2), pp. 15-20. Nica, A., Suchanek, F.M. and Varde, A., 2007. Emerging multidisciplinary research across database management systems. [pdf] European Research Council. Available at: [Accessed 24 June 24, 2014]. Yang, H., 2003. Comparing relational database designing approaches: Some managerial implications for database training. Industrial Management & Data Systems, 103(3), pp.150 – 166. Zhang, Q., 2001. Object-oriented database systems in manufacturing: selection and applications. Industrial Management & Data Systems, 101(3), pp. 97-105. Read More