Association rules mining on MapReduce

Abstract

Many governments are considering adopting the smart city concept in their cities and implementing big data applications that support smart city components to reach the required level of sustainability and improve the living standards. Smart cities utilize multiple technologies to improve the performance of health, transportation, energy, education, and water services leading to higher levels of comfort of their citizens. One of the recent technologies that have a huge potential to enhance smart city services is big data analytics which have many challenges for analyzing urban datasets such as data volume. The user's requirement for information increases from minute to minute and the storage capacity gives an enormous size of data making it difficult to recognize useful information which can be understandable and process by human brain for decision making. Data mining and knowledge discovery has emerged to extract useful, hidden and unknown patterns and knowledge from large database, It is big question what will a data collector do with this massive size of data? Answer is simple person wants knowledge from this data which satisfies users need. This paper deals with the study of different big data analytics tools provide by different companies, then we will focused on the association rules data mining technology. The advantages and disadvantages of Apriori algorithm which will be deeply analyzed, then the functioning of Hadoop and MapReduce Process finally, the performance of this algorithm is compared with the experimental results applied on different datasets taken from traffic accidents.