The age of big data is now coming. But the traditional data 
analytics may not be able to handle such large quantities of data. The question that arises now is, how to develop a high performance platform to efficiently analyze big data and 
how to design an appropriate mining algorithm to find the useful things from big data. To deeply discuss this issue, this paper begins with a brief introduction to data analytics, followed by the discussions of big data analytics. 
Some important open issues and further research directions will also be presented for the next step of big data analytics.

/pdf/big-data-analytics-a-survey-2m5v4g89fb.pdf

Big data analytics: a survey

http://ieb.whut.edu.cn/download.jsp?address=/upload%2Fdoc%2F20121228%2F%5B2012j%5Dtowards+secure+mobile+cloud+computing+a+survey.pdf

Towards secure mobile cloud computing: A survey

Recommender systems are one of the most successful applications of data mining and machine-learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each user-item-pair only one interaction (e.g., a rating) is considered. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works have shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process.In this work, we review existing works that consider information from such sequentially ordered user-item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call sequence-aware recommender systems, and outline open challenges in the area.

/pdf/sequence-aware-recommender-systems-2i004vozij.pdf

Sequence-Aware Recommender Systems

Recommender systems are one of the most successful applications of data mining and machine learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each user-item-pair only one interaction (e.g., a rating) is considered. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works have shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process. In this work we review existing works that consider information from such sequentially-ordered user- item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call sequence-aware recommender systems, and outline open challenges in the area.

Load-balancing algorithms in cloud computing

Many parallelization techniques have been proposed to enhance the performance of the Apriori-like frequent itemset mining algorithms. Characterized by both map and reduce functions, MapReduce has emerged and excels in the mining of datasets of terabyte scale or larger in either homogeneous or heterogeneous clusters. Minimizing the scheduling overhead of each map-reduce phase and maximizing the utilization of nodes in each phase are keys to successful MapReduce implementations. In this paper, we propose three algorithms, named SPC, FPC, and DPC, to investigate effective implementations of the Apriori algorithm in the MapReduce framework. DPC features in dynamically combining candidates of various lengths and outperforms both the straight-forward algorithm SPC and the fixed passes combined counting algorithm FPC. Extensive experimental results also show that all the three algorithms scale up linearly with respect to dataset sizes and cluster sizes.

Apriori-based frequent itemset mining algorithms on MapReduce

The importance of the data stored in the smart phones is increased as more applications are deployed and executed. Once the smart phone is damaged or lost, the valuable information treasured in the device is lost altogether. If cloud storage can be integrated with cloud services for periodical data backup of a mobile client, the risk of data lost can be minimized. However, the important data might be uncovered by a malicious third party during retrieval or transmission of information using wireless cloud storage without proper authentication and protection. Therefore, in this paper, we design an archive mechanism that integrates cloud storage, hybrid cryptography, and digital signatures to provide security requirements for data storage of mobile phones. Our mechanism not only can avoid malicious attackers from illegal access but also can share desired information with targeted friends by distinct access rights.

Secure cloud storage for convenient data archive of smart phones

Sequential patterns in customer transactional databases are commonly mined for E-Commerce recommendations. In many practical applications, the absence of certain item-sets and sequences could have important implications. Mining frequent sequences comprising not only the occurrence but also the absence of certain sequences will increase the accuracy of product recommendations. A sequential pattern containing at least one absent item set is called a negative sequential pattern. In this paper, we formulate the problem of negative sequential pattern mining by introducing practical constraints and propose an algorithm called PNSP for the mining. The discovered patterns can then be more interesting and effective to use. The experimental results show that PNSP may discover negative sequential patterns for practical E-commerce applications.

Mining Negative Sequential Patterns for E-commerce Recommendations

High utility pattern mining using the maximal itemset property and lexicographic tree structures

Entity resolution (ER), which detects records referring to the same entity across data sources, is a long-lasting challenge in database management research. The sheer volume of data collections today calls for the need of a blocking-based ER algorithm using the MapReduce framework for cloud computing. Most studies on blocking-based ER assume that only one blocking key is associated with an entity. An entity in reality may have multiple blocking keys in some applications. When the entities have a number of blocking keys, ER can be more efficient since two entities can form a similar pair only if they share several common keys. Therefore, we propose a MapReduce algorithm to solve the ER problem for a huge collection of entities with multiple keys. The algorithm is characterized in the combination-based blocking and the load-balanced matching. The combination-based blocking utilizes the multiple keys to sort out necessary entity pairs for future matching. The load-balanced matching evenly distributes the required similarity computations to all the reducers in the matching step so as to remove the bottleneck of skewed matching computations for a single node in a MapReduce framework. Our experiments using the well-known CiteSeerX digital library show that the proposed algorithm is both efficient and scalable.

Sue-Chen Hsueh

Papers

Apriori-based frequent itemset mining algorithms on MapReduce

Secure cloud storage for convenient data archive of smart phones

Mining Negative Sequential Patterns for E-commerce Recommendations

High utility pattern mining using the maximal itemset property and lexicographic tree structures

A load-balanced mapreduce algorithm for blocking-based entity-resolution with multiple keys