Choosing a suitable data-analytics software for a company’s operations

Abstract. In this research, the purpose was to study the different factors that contribute to a company’s consideration around choosing a suitable data-analytics software to be adopted into their operations. The research was based around the notion that there currently exists a gap between the information technology and the companies, where valuable data is being wasted by the companies at the cost of their competitiveness due to their limited capabilities in data analytics. Data-analytics software were noted to be potentially valuable for the companies by being able to help bridging the gap between them and the information technology by allowing them to make more use out of data in their operations, but this was not to be taken for granted at any situation due to the overall complexity and extent of the phenomenon. The research was conducted by performing a literature review on the existing scientific literature around the phenomenon and a case study, which provided a concrete example from a real-world setting. The combined results from these research methods were then analyzed together in a further analysis to identify relevant factors and describe their possible effects as opportunities and challenges for every company to consider, which may eventually steer their choice of a suitable data-analytics software into one direction or another. This research tries to provide better understanding around this process, which is supposed to lead to a specific choice and uncover the reasoning behind it. This can essentially present useful guidelines for the companies interested in adopting data-analytics software into their operations. The results of the research pointed out that there are plenty of different options for a company to choose from, which can prove out to be suitable for their operations. The choice itself is eventually based on the company’s own characteristics and requirements, which may require different forms of evaluations depending on their nature. In addition, it was emphasized that users should be given a central role in the consideration, because they are eventually responsible for the creation of value through data-analytics software and they are significantly being affected by the quality of the software. The opportunities and challenges also presented important points to consider, because their potential effects can easily be overlooked by many companies. The results emphasized that companies should approach the choice with careful consideration from a unique perspective, where the presented issues can essentially be utilized as useful guidelines to increase their chances of finding a suitable data-analytics software for their operations and eventually gaining value from it. However, it can be argued that data-analytics software are still surrounded with a fair amount of uncertainty relating to the companies’ return of investment, which suggests that there is still a lot of work to be done in this field

Characterizing unpredictable patterns in Wireless Sensor Network data

Wireless Sensor Network (WSN) monitoring takes a primary role in many industrial and research processes. Huge amounts of WSN sensor readings are nowadays available and can be analyzed to discover fruitful knowledge. This paper focuses on analyzing historical WSN sensor readings to identify the combinations of sensors whose readings show an unexpected trend. Although significant variations of single sensor readings may be easily detected, discovering correlations between multiple sensor readings is challenging without using advanced data analytics tools. To tackle this issue, we present an itemset-based data mining approach to analyzing WSN data. It identifies the combinations of sensors (of arbitrary size) whose readings are unexpectedly low in a given time period. Since the readings acquired by multiple sensors may decrease in an alternate fashion, the discovered patterns provide new information compared to single sensor analysis. To make the mined patterns manageable by domain experts for manual inspection, the mining algorithm is driven by spatial constraints defined on the WSN topology. The experimental results, achieved on real WSN data, demonstrate the effectiveness of the proposed approach in detecting heating system malfunctioning