Assignment 02 - Data Governance and Data Management

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Word Count: 1020 IT Infrastructure Management PG S-ITC540_202030_SM_I Assignment 02 Assessment item 02 - Data Governance and Data Management Student Name: Chathurika Prasadi Indrasiri Ranpati Devage Student ID: 11703073 Lecturer Name: Dr Michael Baron Date: 10/04/2020

2 Q 01 IS concepts and classification In learning system, concept and classification is know as most popular in the present days. According to Robert Gagne (1985), he showed that ideas are the structures of most intellectual assistances that we have. Concepts are involved in the ability to use rules. The ethics define the happenings of things. These "things" are perceptions that are simpler proportion of the element and should be learned earlier a student can master the principle (Emmanuel, 2000). As per to Emmanuel (2000) the methods define how to do modify in things (to accomplish an aim). These "things" are concepting that are same proportion of the way and it should be learned before learning the process. Big data analytics As per to Philip Russom (2011) the word of “big data” re late to data that is much great, rapid or complicate that it’s hard or unable to progress using outdated procedures. Further he releveled that big data analysis is the often-complex process of looking at large and diverse data sets or big data to find information. Big data analysis is based on special software and analysis systems as well as high performance computer systems and offers many business benefits, including new sales opportunities, more effective marketing, better customer service and efficiency higher operational. (Philip Russom, 2011) Change data capture As per to Stone et al (2007) data collection in the event of a change is a data integration approach that enables businesses to get more value from their data and integrate and analyse data faster and use less system resources. Also, CDC eliminates the need to update bulk loads and bulky storage windows by allowing incremental loading or real-time transfer of data changes to the data warehouse. (Stone et al, 2007). It can also be used to populate real-time Business Intelligence dashboards, synchronize data across geographically distributed systems, and simplify database migration without interruption. (Norbert, 2004)

3 ETL, RDBMSs. As per to Dumin et al (2015) has mentioned the ETL as extract transform and load and it includes function of reading the data from a database. The transform function converts the extracted data into a format suitable for analysis and archiving, and the load function takes care of writing the transformed data to the new application / database. (Dumin et al, 2015) Tilmann et al 2015 presented RDBMS as "Relational Database Management System" and is a DBMS specially developed for relational databases in his research. RDBMS is a subset of DBMS. A relational database states to a database in which the information is stored in an organised format. Hadoop and MapReduce Husain et al (2017) mentioned in his research as Apache Hadoop is a group of expand source of software utilities which simplify the manipulation of a network of lots computers to resolve problems with big amounts of information and processing. Further Husain et al (2017) mentioned that it delivers an outline for expansive archiving and managing of big data using the MapReduce profile. MapReduce is a process of method & program structure for delivered processing sourced on Java. (Husain et al, 2017). The MapReduce algorithm have two its important activities, it is mapping & minimizing. The main benefit of MapReduce process is which it is much easy to change the size of data processing on several computer nodes. (Husain et al, 2017). Internet of Things Luigi et al (2010) mentioned the Internet of Things (IoT) states to billions of physical strategies around the world which are presently linked to the internet and collect & share information. With the advent of fabulous affordable computer chips and the ubiquity of wireless networks, it is easy to try whatever from somewhat minor like a pill into somewhat as large as an airplane as portion of the IoT. The word IoT is mostly used for structures which generally do haven’t an internet connection & is able to do connect with the system regardless in human actions. (Luigi et al, 2010).

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4 Mashup According to Jin Yu et al (2008) mashups are about simplicity, usability and ease of access. This simplicity has the advantage over functional integrity or maximum extensibility. Mashup has become one of the most popular buzzwords in web applications, and many companies and institutions are rushing to come up with mashup solutions. Web mashups are the applications in web which are made by combining content, presentation, or application functionality from disparate Web sources. (Jin Yu et al 2008). They object to associate these sources to create useful new applications or service facilities. Q 02 Type 3G 4G 5G Year of released 1990 – 2000 (Gawas , 2015) 2000 – 2010 (Gawas , 2015) Could be in 2020 or in near future (2015 onwards) (Gawas , 2015) Conditions Higher data transmission at lower cost. (Gawas , 2015) High quality, high capacity, reduced price for voice calls and data prices. (Gawas , 2015) It is a new generation commercial network which is being tested. (Gawas , 2015) Data Bandwidth & Speed Speeds up to 2 Mbps Use large satellite connections to telecommunication towers. (Gawas , 2015) Speeds up 200 Mbps to 1Gbps IP networks that enables even faster data transfer rates. (Gawas , 2015) Speed up to 1Gbps and higher. (Gawas , 2015)

5 Q 03 a) As per to the journal article of use of big data in drug development from Qian et at (2019) it is estimated that 90 to 95% of new drugs fail. The development of a new drug takes more than a decade and can be priced as many billion dollars. The outcome is which mistakes frequently happen late in the improvement process and cost time, money and life. (Qian et al, 2019). b) Private and public partnership with big data technology are created biomedical analysis probable. (Qian et al, 2019). Advances in biotechnology provide vast information on the biological reasons of the illness, and the collection and analysis of this large amount of data are allowed by big data technology. Public-private partnerships between drug manufacturers, governments and university researchers bring together experts in fields of biology, bioinformatics, genomics, and big data to invent the chances of medicines. (Bloomberg, 2018) c) Investing money for big data technologies for drug manufacturers are able to prevent the growth of medicines which aim the incorrect biological tracks without wasting lots of money on research. (Qian et al, 2019). d) As per to Bloomberg (2018) mentioned in Fortune magazine that exchanging information and strengthening cooperation in each other’s , it is possible to accelerate the expansion of new and state-of-the-art medicines, reduce expenses & save human s’ lives. References a, L. A., Iera, A. b., & c, G. M. (2010). The Internet of Things. Article in Press . Big Data Analytics. (2011). Fagbohun, O. O. (2014, May-June). Comparative Studies on G,4G & 5G wireless technology. Journal of Electronics and Commiunication Engineering (IOSR-JECE), 9 (3 version), 88-94. Retrieved April 07, 2020, from www.iosrjournals.org

6 Gawas, A. U. (2015). An overview on Evolution of Mobile Wireless Commiunication Networks 1G - 6G. International Journal on Recent Innovation trends, 3 (Computing and Commiunication), 3130-3133. Retrieved April 8, 2020, from http://www.ijritcc.org Husain, M. F., Doshi, P., Khan, L., & Thuraisingham, B. (n.d.). Stroage and Retrival of Large RDF Graph Using Hadoop and MapReduce. Qian, T., Zhu, S., & Hoshida, Y. (2019). Use of big data in drug development for precision medicine: an update. Expert Rev Precis Med Drug Dev. 2019 4(3) , 189-200. Sahiet, D., & Asanka, P. D. (2015). ETL Framework Design for Database in Data Warehousing. International Journal of Research in Computer Applications and Robotics ISSN 2320 - 7345 , 67-75. Seruga, B., Ocana, A., Amir, E., & Tannock, I. F. (n.d.). Failures in Phase III : Causes and Consequences. Stone, A., Shiffman, S. S., Atienza, A., & Nebeling, L. (2004). Retrospective and Concurrent Self Report: The Rationale for Real-Time Data Capture. New York: Oxford University. Torquebiaua, E. F. (2000). A renewed perspective on agroforestry concepts and classification. Academie des science , 1009-1017. Yu, J., Benatallah, B., Casati, F., & Daniel, F. (2008). Understaing Mashup Development.

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