- Title
- Semantic manipulation and business context in big data analytics
- Creator
- Dinh, Loan
- Date
- 2018
- Type
- Text; Thesis; PhD
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/169920
- Identifier
- vital:14057
- Identifier
- https://library.federation.edu.au/record=b2770507
- Abstract
- Business organisations receive a huge amount of data from many sources every day. These data are known as big data. Since they are mostly unstructured, big data creates a complex problem of how to capture, manage, analyse and then derive meaningful information from them. To deal with the challenges that big data has brought, this research proposes a new technique in big data analytics in the business area to integrate semantically meaningful information relevant to textual queries and business context. To achieve this aim, this study makes three major related contributions. Firstly, the relationship between business processes and strategies is established using the concept of a rule-based inference model via facts and annotations. This relationship is required to determine the importance of a big data query for a business organisation. Secondly, we introduce approaches to determine the significance level of a query, by incorporating the processstrategy relationship, process contributions and priority of business strategies. Thirdly, the proposed data analytic technique embeds business context into the bedrock of data collection and analysis process. The first two contributions were implemented using Python programming language including the Pyke package (Pyke is built in the Python environment and has an artificial intelligence tool for the development of expert systems) and their performances were analysed based on a business use case. The last contribution was implemented mainly in the Hadoop and Java programs. Results show that the first contribution successfully establishes the processstrategy relationship, the second calculates the significance level of a query in relation to a business organisation, while the third reveals the huge impact of query significance level and business context on big data collection and captures deep business insights.; Doctor of Philosophy
- Publisher
- Federation University Australia
- Rights
- Copyright Loan Dinh
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Big data; Analytics; Business processes; Business strategies; Business organisation; Business context; Process-strategy relationship
- Full Text
- Thesis Supervisor
- Karmakar, Gour
- Hits: 1327
- Visitors: 1351
- Downloads: 166
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Australian Digital Thesis | 4 MB | Adobe Acrobat PDF | View Details Download |