Many difficulties exist when defining and deciding which requirements should be implemented first in an ultra-large-scale multi-stakeholder involved system. This often leads to system failure and product dissatisfaction. This paper established a suitable method supporting more precise and accurate decision-making in prioritizing requirements. We collected and analyzed a large number of software requirements in a case study, which was based on real-life practices and processes. Structured interviews and questionnaires were used to collect data from 600 stakeholders. We formulated a model based on the analyzed requirements using the CBRanking, and the MACBETH approaches. We ranked the requirements and considered the requirements' relative importance according to the stakeholders' opinions. Thus, a hybridized mathematical model was proposed for prioritizing these functional requirements and evaluated its performance for consistency and completeness. The results showed the software's best functional requirements concerning the customers' expectations.
Information is knowledge if it is rightly applied. Information are stored with different formats in databases but retrieving such from different documents has been a challenge. People want ready-made information for the purpose of decision making in minimal time and thereby crave for summary of information. Automatic summarization helps in mining data and delivering timely and cogent information to users. These systems attempt to address the issue of data mining using different summarization methods. This paper discusses existing methods and state of the art in automatic summarisation system from recent articles. Achievement and challenges involve are also discussed.
Data warehousing and cloud computing are modern trends in computing businesses. Data warehouse (DWH) is subject-specific, time-changing, non-volatile, integrated collection of data and it is a process that helps decision maker in the process of informed decision making. It is also an integrated software component of the cloud which provides support for timely and accurate response to complex queries. The complex analytics involving huge amounts of data with the help of tools such as: Online Analytical Processing (OLAP) and data mining are built on DWH model. Similarly, cloud computing provides a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider’s interaction. These computing infrastructures when allowed to work together can provide decision makers with immense benefits at minimal costs. Therefore, it is pertinent to consider the contending issues and challenges faced by bridging the gap between DWH and cloud computing as well as proffer possible solutions. The future directions and conclusions are also pointed to and drawn from the paper.
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