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In many applications, top-k query is an important operation to return a set of interesting points in a potentially huge data space. It is analyzed in this paper that the existing algorithms cannot process top- k query on massive data efficiently. This paper proposes a novel table-scan-based T2S algorithm to efficiently compute top-k results on massive data. T2S first constructs the presorted table, whose tuples are arranged in the order of the round-robin retrieval on the sorted lists. T2S maintains only fixed number of tuples to compute results. The early termination checking for T2S is presented in this paper, along with the analysis of scan depth. The selective retrieval is devised to skip the tuples in the presorted table which are not top-k results. The theoretical analysis proves that selective retrieval can reduce the number of the retrieved tuples significantly. The construction and incremental-update/batch-processing methods for the used structures are proposed in this paper. The extensive experimental results, conducted on synthetic and real-life data sets, show that T2S has a significant advantage over the existing algorithms. [ABSTRACT FROM AUTHOR]

This paper presents an application for generating a dynamic selection screen in ABAP and the need and advantages of this solution instead of the manual modification of the coding itself for adding one or more parameters in the whole logic of the program. Depending on data found in a customizing table, we would like to generate a program with a selection screen, containing select options based on the criteria specified in the customizing table. The presented solution can be adapted to any dynamically generated ABAP program, according to the desired selection screen elements, and may be used as a template even for other programs. The advantages of the proposed solution are also presented along with the disadvantages, underlining the particularities of the ABAP programming language. [ABSTRACT FROM AUTHOR]

High utility itemset mining has recently emerged to address the limitations of frequent itemset mining. It entails relevance measures to reflect both statistical significance and user expectations. Whether breadth-first or depth-first search algorithms are employed, most methods generate new candidates by 1-extension of existing itemsets (i.e., by adding only one item to verified itemsets to generate new potential candidates). As an alternative to 1-extension, we introduce an expansion method based on binary partition. We then define the transaction utility list and key support count and discuss a new pruning strategy. Based on the new itemset expansion method and pruning strategy, we propose an efficient high utility itemset mining algorithm called BPHUI-Mine (Binary Partition-based High Utility Itemsets Mine). Tests on publicly available datasets show that the proposed algorithm outperforms other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]