Sample MITS4003 Database Systems Report 3

MITS4003 Database Systems Report 3 Sample

Objectives(s)

This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment is designed to improve student knowledge through further research on recent trends to demonstrate competence in tasks related to modelling, designing, implementing a DBMS, Data Warehousing, Data management, Database Security. Also, to enhance students experience in researching a topic based on the learning outcomes they acquired during lectures, activities, assignment 1 and assignment 2. Furthermore, to evaluate their ability to identify the latest research trends and writing a report relevant to the Unit of Study subject matter. This assessment covers the following LOs.

1. Synthesize user requirements/inputs and analyse the matching data processing needs, demonstrating adaptability to changing circumstances;

2. Develop an enterprise data model that reflects the organization's fundamental business rules; refine the conceptual data model, including all entities, relationships, attributes, and business rules.

3. Derive a physical design from the logical design taking into account application, hardware, operating system, and data communications networks requirements; further use of data manipulation language to query, update, and manage a database

4. Identify functional dependencies, referential integrity, data integrity and security requirements; Further integrate and merge physical design by applying normalization techniques;

5. Design and build a database system using the knowledge acquired in the unit as well as through further research on recent trends to demonstrate competence in various advanced tasks with regard to modelling, designing, and implementing a DBMS including Data warehousing, Data Management, DB Security.

Note: Group Assignment. Maximum 4 students are allowed in a group.

INSTRUCTIONS

These instructions apply to both the Report and Presentation assessments. For this component you will be required to select a published research article / academic paper which must cover one or more of the topics including Database modelling, designing, and implementing a DBMS including Data warehousing, Data Management, DB Security, Data Mining or Data Analysis. The paper you select must be directly relevant to these topics. The paper can be from any academic conference or other relevant Journal or online sources such as Google Scholar, academic department repositories etc. All students are encouraged to select a different paper; and it must be approved by your lecturer or tutor before proceeding. In case two groups are wanting to present on the same paper, the first who emails the lecturer or tutor with their choice will be allocated that paper.

Report - 20% (Due week 12)

For this component you will prepare a report or critique on the paper you chose as mentioned above. Your report should be limited to approx. 1500 words (not including references).

Use 1.5 spacing with a 12-point Times New Roman font. Though your paper will largely be based on the chosen article, you can use other sources to support your discussion. Citation of sources is mandatory and must be in the Harvard style.

Your report or critique must include:

Title Page: The title of the assessment, the name of the paper you are reviewing and its authors, and your name and student ID.

Introduction: A statement of the purpose for your report and a brief outline of how you will discuss the selected article (one or two paragraphs). Make sure to identify the article being reviewed.

Body of Report: Describe the intention and content of the article. Discuss the research method (survey, case study, observation, experiment, or other method) and findings. Comment on problems or issues highlighted by the authors. Discuss the conclusions of the article and how they are relevant to what you are studying this semester.

Conclusion: A summary of the points you have made in the body of the paper. The conclusion should not introduce any ‘new’ material that was not discussed in the body of the paper. (One or two paragraphs)

References: A list of sources used in your text. Follow the IEEE style. The footer must include your name, student ID, and page number.

Solution

Introduction

The article “An overview of end-to-end entity resolution for big data.“ will give a brief description of the entity resolution for big data which is being rebuked and critically analyzed. The paper will provide a comprehensive view that includes the field of entity resolution and focus on the application with context to big data. For Assignment Help, This research article will propose the framework or the entity resolution on behalf of big data which entitles the identification and collapse of records in real-world entities. This Framework will also design and challenge the proposed big data on behalf of different considering techniques and evaluating the proposed Framework with the help of real-world data sets. This article will cover topics such as database modeling data management and data analysis. It will be more relevant to the topics that will be presented by the framework to design and implement the system which can handle the challenges of Designing and considering the entity resolution more accurately and efficiently.

The intention of the Article

The article is likely to produce the intention of the comprehensive view and analysis which will be conducted on the end entity resolution and the techniques that are specifically organized and implemented for the big data scenarios.

- The research also leads to the importance and the challenges that are been faced while using data resolution to solve the big data issues. The accurate integration of the data and cleansing is been applied so that the impact of data characteristics can be processed on the entity resolution [6].

- The article also explores the Different techniques and approaches that will be used in resolving the big data which will be implemented with the help of rule base methods such as machine learning algorithms or probabilistic models to design and handle the big data.

- The data preprocessing is also been covered for the effective and necessary entity resolution in the big data. This also includes the normalization and analysis of the data warehouse to propose the data modeling for high-quality results.

- The article also optimizes the scalability and the efficiency of the data that is been analyzed to explore the techniques in parallel and distributed processing. The data partitioning with the entity resolution process plays a major role when it comes to the large-scale data set.

- The evaluation and the applications of the case study also play a major role in the resolution of the techniques that leads to the successful implementation of big data scenarios such as various domains of Healthcare or e-commerce.

Survey

- The author has specified the big data error concerning the government and the specific organization that increases the internal and external aspects.

- The Entity resolution mainly AIMS to the real-world entity that is been structured and stored in the relational tables of the big data to consider the scenarios.

- The author has illustrated the description of the movie directors and the places from the different knowledge bases and the entity description is being defected in the tabular format.

- The classification of the pairs and the description is being assumed to process the in-compasses task and indexing to match the data.

Figure 1 Movies, Directors, and Locations from DBpedia (blue) and Freebase (red). Note that e1, e2, e3 and e4 match with e7, e5, e6 and e8, respectively.

- The author includes the survey about the big data characteristics which shows the algorithm and the implemented task and the workflow of the data. This includes the volume variety velocity as the characteristics of the big data [2].

Case Study Based on Data Modelling and Data Analytics

- The big data entry resolution considered the case study about the continuous concern and improving the scalability of this technique for increasing the volume of entities using the massively parallel implementations with the help of data Modelling and analysis.

- The Entity description is being evaluated with high veracity which is been resolved by matching the Data Analytics value and traditionally duplicating the techniques. With the help of analysis the conceived processing of the structure data can be educated pre-process to data warehouse and enhanced the blocking keys to rule the different types of challenging data.

- The below figure depicts the different types of similarities and the entities with the benchmark data set and considered the restaurant or other key parameters that are involved with the dot corresponding to each other of the matching pair [4].

- The horizontal accessing of the similarity is described with the vertical and maximum similarities are based on the entity neighbors. The value-based similarities are being proposed on the big data entities which are being used to improvise the data quality and the data modeling techniques to compile the integrity of the data management.

Figure 2 Value and neighbor similarity distribution of matching entities in 4 established, real-world datasets.

Observations

- Data Modelling

The article considered data modeling as an approach for entity resolution in the context of big data. As this covered the techniques of representing the structure data which help in capturing the attributes and relationship with the attitude resolution. The schema design and the data integration also play a major role in the data representation of formulating big data [1].

- Data Analysis

The technique leads to the discussion and the observation of measuring the feature extraction and statistical methods which help in comparison the matching the entities. This also covers the algorithm which is based on machine learning and the data mining to the employee or deploying the Entity resolution with the clustering and classification models.

- Data Management

The Strategies and processing of the large data is been managed during the entity resolution process. This technique leads to the handling of noise and inconsistency with the missing values of the big data full stop this also leads to the exploring the index and the storage mechanism which help in facilitating the retrieval of the matching entity full stop the parallel and the distributed processing leads to the scalability and challenging resolution of the big data.

Conflicts or Issues

- Heterogenous Data Sources

The environment of the analyzing technique and big data necessitate the diversification of sources, such as databases and sensor networks. The entities' integrity and rec counseling have been viewed as a problem or conflict to suggest difficulties arising from differences in data formats and schemas [5].

- Dimensionality

The numerical attribute or features needed to handle dimensional data are the data and entities' dimensions. In order to avoid the dimensionality curse, the most effective method is taken into consideration, as are the featured engineers and other computations.

- Computational Efficiency

The entity resolution is being performed and processed with the computational demand of the algorithms which are considered as the contract of parallel processing technique. This distributed computational and the Framework are necessary which achieve the scalability and entity of the big data.

Similarities of research with the Study of Semester

- As the research is been considered the similarity of developing the knowledge regarding the user requirements and analysis to match the data and processing the needs to demonstrate the circumstances.

- With the help of this research, the enterprise data model and the reflects fundamental business rule is being conceptually Defined by the data modeling which includes the attribute and the business rules.

- The physical designing and the logical designing is been taken under the implementation to account for the communication with the network requirement and manipulating the language to manage the database [3].

- The identification of the functionality and its dependency is the referential integration and the Data integrity to provide the security requirements for merging the physical design and applying the normalization technique.

- The building of the database and the knowledge is being acquired by further research to demonstrate the competency in the Advanced Task of modeling and designing the implementation of data warehouse and Management.

Conclusion

The report has deeply explained about the theoretical aspect of the and to end resolution of the big data with the implementation methodology of data Modelling and analysis to manage the data. The specific methodology and case study has been considered in the article with the general representation of the concluded entity and algorithms that is been applied. The problem has been observed with the recent years of data-intensive and the description of the real world entities with the government or the corporate-specific data sources for stop the view of entity resolution with the engineering aspect and the task has also been implemented as a theoretical aspect of considering the certain algorithms. The big data and the area of open-world systems have also allowed the different blocking and matching algorithms to easily integrate the third-party tools for data exploration and sampling the Data Analytics.

References