A rule based inference model to establish strategy-process relationship
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew
- Date: 2017
- Type: Text , Conference proceedings
- Relation: 30th International Business Information Management Association Conference - Vision 2020: Sustainable Economic development, Innovation Management, and Global Growth, IBIMA 2017; Madrid, Spain; 8th-9th November 2017 Vol. 2017-January, p. 4544-4556
- Full Text: false
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- Description: An effective relationship between business processes and their relevant strategies helps enterprises achieve their goals. As a business organisation changes quickly, business processes implement their relevant business operations for efficiency. It is important to know which business process achieves which business strategies dynamically. To the best of our knowledge, there exists a framework which aims to automatically determine the strategy-process relationship (Morrison et al. 2011). However, this framework can only work when the effect of the business process is known, but it is difficult to determine such effect accurately. Moreover, by optimising business processes to satisfy business strategies, higher efficiency may be achieved but there is a high chance of losing discriminative information. It therefore creates certain level of uncertainty in achieving accurate strategy-process relationship. To reduce this uncertainty and determine the relationship accurately between business processes and their relevant strategies as defined by business domain experts, in this paper, we introduce a rule-based inference model. This model not only helps business organisations realize which business processes need to be involved for the organisation to achieve their goals when strategies are made, but also reduces the possibility of losing important details from business process optimisation. We have developed a business case to validate our proposed model and the results show that our model can infer the relation accurately for each rule defined for the related business case.
Significance level of a big data query by exploiting business processes and strategies
- Authors: Dinh, Loan , Karmakar, Gour , Kamruzzaman, Joarder , Stranieri, Andrew
- Date: 2018
- Type: Text , Conference paper
- Relation: 13th Joint International Baltic Conference on Databases and Information Systems Forum and Doctoral Consortium, Baltic-DB and IS Forum-DC 2018 Vol. 2158, p. 63-73
- Full Text: false
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- Description: Querying data is one of the most frequent activities in business organisations. The tasks involving queries for big data collection, extraction and analysis have never been easy, because to obtain the high quality responses, the expected outcome from these tasks need to be more accurate and highly relevant to a business organisation. The emergence of big data era has further complicated the task. The enormous volume of data from diverse sources and the variety of queries impose a big challenge on business organisations on how to extract deep insight from big data within acceptable time. Determining significance levels of queries based on their relevance to business organisations is able to deal with such challenge. To address this issue, up to our knowledge, there exists only one approach in the literature to calculate the significance level of a query. However, in this approach, only business processes are considered by manually selecting weights for core and non-core business processes. As the significance level of a query must express the importance of that query to a business organisation, it has to be calculated based on the consideration of business strategic direction, which requires the consideration of both business processes and strategies. This paper proposes an approach for the first time where the significance level of a query is determined by exploiting process contributions and strategy priorities. The results produced by our proposed approach using a business case study show the queries that are associated with more important business processes and higher priority strategies have higher significance levels. This vindicates the application of the significance level in a query to dynamically scale the semantic information use in capturing the appropriate level of deep insight and relevant information required for a business organisation. Copyright © 2018 for this paper by the papers' authors.