ABSTRACT In this era of big data, knowledge representation is challenged by the complexity, variety, velocity and volume of data. This project is aimed to produce an engine that is able to utilise the Information Retrieval in the form of Data, Information, Knowledge, and Wisdom (DIKW) hierarchy. The method of constructing this engine is by assimilating the statistical analysis with semantic network and ontological arguments in the platform of Web Ontology Language (OWL). Data types involved include numerical data, geospatial mapping, and global meteorological data. This smart retrieval engine is expected to return a prescriptive wisdom of subject matter.

APPLICABILITY A case study was carried out to the flood disaster   in the state of Kelantan, Malaysia. This disaster occurrence was seen to be an annual event that many researchers keep coming back to the state to study on its causes and implications.  The flood is always happening during the North-Eastern Monsoon which usually takes place between October to February every year. For this case study various sources of data were taken for computation which have different data types including the numerical data, strings, images, and geospatial. Hence making them the big data.

NOVELTY This project novelty is upon its structure that it contains the following properties and coding: 1.  It computes the various types of big data from different type of

sources. 2.  It builds a smart retrieval engine by utilizing the approach of statistical

analytics and ontological arguments. 3.  It produces the prescriptive data analytics in the form of DIKW

hierarchy.

COMMERCIALISATION This project is expected to run an online prediction engine with the ability of prescriptive analytics in the form of DIKW hierarchy. To achieve this result, the approach of statistical analytics and ontological arguments were utilised as the smart engine for Information Retrieval. Knowledge representations were defined by propositional logic and predicate calculus. This smart engine is feasible with the big data of the case study carried out in terms of its volume, variety, velocity, veracity and value. The performance of this engine is determined by the ability of its computation to translate the input data into more prescriptive analytics at different levels of data particularly the Information, Knowledge, and Wisdom.

Smart Retrieval Engine for Prescriptive Big Data Analytics in DIKW Hierarchy Environment

by: 1Aziyati Yusoff, 2Salman Yussof, 3Norashidah Md Din, and 4Samee Ullah Khan

1College of Engineering, Universiti Tenaga Nasional (UNITEN), Malaysia

2College of Information Technology, Universiti Tenaga Nasional (UNITEN), Malaysia 3College of Graduate Studies, Universiti Tenaga Nasional (UNITEN), Malaysia

4Electrical and Computer Engineering Department, North Dakota State University (NDSU), USA

Figure 1. The smart retrieval engine project architecture.

Figure 2. The case study is about the big data from different sources and data types of flood disaster in Kelantan, Malaysia.

Figure 3. The Semantic Network of Flood Big Data in the Platform of Ontology and Statistical Analysis

Figure 4. The Decision Tree and DIKW Level of Abstraction for a Disaster Incident