Biblio

As we are living in the era of big data, high volumes of wide varieties of data which may be of different veracity (e.g., precise data, imprecise and uncertain data) are easily generated or collected at a high velocity in many real-life applications. Embedded in these big data is valuable knowledge and useful information, which can be discovered by big data science solutions. As a popular data science task, frequent pattern mining aims to discover implicit, previously unknown and potentially useful information and valuable knowledge in terms of sets of frequently co-occurring merchandise items and/or events. Many of the existing frequent pattern mining algorithms use a transaction-centric mining approach to find frequent patterns from precise data. However, there are situations in which an item-centric mining approach is more appropriate, and there are also situations in which data are imprecise and uncertain. Hence, in this paper, we present an item-centric algorithm for mining frequent patterns from big uncertain data. In recent years, big data have been gaining the attention from the research community as driven by relevant technological innovations (e.g., clouds) and novel paradigms (e.g., social networks). As big data are typically published online to support knowledge management and fruition processes, these big data are usually handled by multiple owners with possible secure multi-part computation issues. Thus, privacy and security of big data has become a fundamental problem in this research context. In this paper, we present, not only an item-centric algorithm for mining frequent patterns from big uncertain data, but also a privacy-preserving algorithm. In other words, we present- in this paper-a privacy-preserving item-centric algorithm for mining frequent patterns from big uncertain data. Results of our analytical and empirical evaluation show the effectiveness of our algorithm in mining frequent patterns from big uncertain data in a privacy-preserving manner.