Complex Mining from Uncertain Big Data in Distributed Environments
Alfredo Cuzzocrea, Carson Kai-Sang Leung, Fan Jiang, Richard Kyle MacKinnon · 2017
298 Big data refer to a wide variety of valuable data of different veracities that are generated or collected at a high velocity with volumes beyond the ability of commonly used software to manage, query, and process within a tolerable elapsed time. On the one hand, big data analytics incorporates various techniques from a broad range of fields, which include cloud computing, data mining, machine learning, mathematics, and statistics. For instance, data mining discovers implicit, previously unknown, and potentially useful information and/or knowledge from data. On the other hand, uncertain big data management represents an active and well-recognized research area where a relevant number of proposals converge. This is due to several reasons, but mostly dictated by the emergence of big data trends as well as the explosion of cloud computing paradigms . Within this wide research context, a leading role is played by the issue of extracting useful knowledge from big data being the uncertain big data setting a critical case to be considered. In our research, we specially focus on two well-known distinct first-class data-mining problems over uncertain big data, namely: (i) frequent itemset mining from uncertain big data and (ii) constrained mining from uncertain big data . We recognize that these subproblems converge into a general problem that we name as complex mining from uncertain big data , for which a plethora of real-life applications and systems can be found. Inspired by these relevant research challenges, we provide in this chapter the following contributions: (i) a comprehensive overview of state-of-the-art literature in the context of the research problem of complex mining from uncertain big data, (ii) an effective and efficient algorithm for supporting tree-based constrained mining of uncertain big data in distributed environments , as well as (iii) another effective and efficient algorithm for supporting MapReduce-based constrained mining of uncertain big transactional data in cloud environments .