International Journal of Advanced Research in Computer Science & Technology (IJARCST 2017) 41 Vol. 5, Issue 3 (July - Sept. 2017) ISSN : 2347 - 8446 (Online) ISSN : 2347 - 9817 (Print) www.ijarcst.com © All Rights Reserved, IJARCST 2013 A Cloud Based Solution For Predicting Strength Level of Residential Houses Using Data Analytics I Kirema D. Mutembei, II Andrew M. Kahonge I School of Computing and Informatics, University of Nairobi, Kenya II School of Computing and Informatics, University of Nairobi, Kenya I. Introduction Kenya and particularly Nairobi, has experienced frequent collapse of buildings. This frequent collapse of residential houses in Nairobi has lead to injuries and deaths in the past is a matter of great concern. The recent collapse being in Kwale - Embakasi, Nairobi City County. Lately, other buildings collapsed in areas like Huruma, makongeni in Thika, Roysambu, along Thika road superhighway just behind TRM mall killing several people and other scores injured [1]. These accidents are avoidable but still they have claimed lives of innocent Kenyans, robbing them of their families and loved ones. The tenants have suffered in such conditions since they are not informed of the condictions of the house before occupying them. Kenyans thus, need to be informed of their safety in the residential homes they live in. An algorithm will be sort that will utilize the construction metrics such as grade of concrete, grade of steel used and the location – wetland, soil type i.e. black cotton or red soil. These metrics will be analysed and used to compute the strength level of a given building. It also will rank the building by some star level to deine if its strong enough and safe for human occupation. The same algorithm will be used to determine the inssuance of occupancy certiicates among other factors. This paper has enhanced ICT usage by developing a prototype for predicting residential building strength level and ensuring residents are well informed of the residential home they wish to live in. The authority will also be informed hence easy to manage the buildings in the County [2]. This paper sought to study the usage of data analytics and cloud computing with view to developing a Software as a Service based residential home classiication service. With among other objectives as to determine dataset analytics techniques used in data analysis, develop an algorithm that will analyse the dataset and provide results based on star rating and to test the algorithm and evaluate techniques of exposing APIs to developers. II. Related Studies The construction industry has been identiied as one area where application of technology in capturing data for use in analysis for determining a house strength is less used if not at all. The dificulties with emerging technologies applications in construction ields where the metrics used, that could function as valuable input information on analytic and predictable methods to provide structural strength level of a building, have not been used [3]. Data mining as a process of identifying interesting patterns from large database [3] [4]. An iterative and exploratory process achieved via automated or manual methods. The primary roles of data mining are: Prediction that involves the use of variables to predict unknown future events or values of a given outcome and description that involves patterns identiication that portray the data used in a signiicant manner. Algorithms are categorized according to various distinctions. These includes classiication methods - used to discover predictive relationships for categorical variables, predictive methods - used to discover predictive relationships for numeric variables and association methods – used in association rule discovery[4], he proposed a conceptual framework for graphical classiication for applications of data mining techniques to inancial fraud detection. Prediction and sequential pattern analysis determines the relationship between dependent and independent variables. This analysis technique was utilized in buildings to predict the strength level of a residential building; some metrics like soil type, concrete grade and steel grade are independent variable while strength level can be dependent variable. Based on such data, a regression curve can be used for strength prediction [6]. Cloud computing paradigm - Software as a Service (SaaS), which this papers’ prototype is based has been used [7]. Cloud Computing can adapt dynamically to different circumstances, like the number of requests, making it making it a good choice for the analytical algorithm deployment. [8-10]. The adoption to use of SaaS for web development [11] and deployment in today’s technologies with [12], saying it is the backbone to Service Oriented Architecture (SOA) [13,14]. Cloud computing goal is to share data, computational calculations and services to the users. The prediction analytical algorithm also needs to share analysed information to its users [15-17]. The basic conceptual framework for the algorithm is as shown in igure one below. Abstract Kenya has experienced frequent collapse of residential houses leading to injuries and deaths. Most tenants are not aware of the status of such buildings. An algorithm to classify and rate the houses based on either structural strength was used. Cloud computing has been used to actualize the algorithm. The algorithm implementation involved irst inding the metrics that determine the strength level of a residential building. Weighted metrics actualized the algorithm and results were displayed in star rating scale where ive star rated house meant was strong enough while one star rated residential house meant a weak building and prone to collapse. Node.js and MongoDB were among technologies used. GPS was implemented to locate exact geolocation building. The prototype results revealed 97 percent accuracy in predicting the strength level of residential buildings. This was so through real time analysis of metrics used to predict the strength level. Keywords Algorithm, Analytics, Metrics, Geographical Information System (GIS), Cloud computing, Software as a service (SaaS).