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We present a new algorithm for mining maximal frequent itemsets from a transactional database. Our algorithm is especially efficient when the itemsets in the database are very long. The search strategy of our algorithm integrates a depth-first traversal of the itemset lattice with effective pruning mechanisms. Our implementation of the search strategy(More)
—We present a new algorithm for mining maximal frequent itemsets from a transactional database. The search strategy of the algorithm integrates a depth-first traversal of the itemset lattice with effective pruning mechanisms that significantly improve mining performance. Our implementation for support counting combines a vertical bitmap representation of(More)
We extend the OLAP data model to represent data ambiguity, specifically imprecision and uncertainty , and introduce an allocation-based approach to the semantics of aggregation queries over such data. We identify three natural query properties and use them to shed light on alternative query semantics. While there is much work on representing and querying(More)
Several recent papers have focused on OLAP over imprecise data, where each fact can be a region, instead of a point, in a multi-dimensional space. They have provided a multiple-world semantics for such data, and developed efficient ways to answer OLAP aggre-gation queries over the imprecise facts. These solutions, however, assume that the imprecise facts(More)
Recent work proposed extending the OLAP data model to support data ambiguity, specifically imprecision and uncertainty. A process called allocation was proposed to transform a given imprecise fact table into a form, called the Extended Database, that can be readily used to answer OLAP aggregation queries.In this work, we present scalable, efficient(More)
We present a performance study of the MAFIA algorithm for mining maximal frequent itemsets from a transactional database. In a thorough experimental analysis, we isolate the effects of individual components of MAFIA, including search space pruning techniques and adaptive compression. We also compare our performance with previous work by running tests on(More)
We present Midas, a system that uses complex data processing to extract and aggregate facts from a large collection of structured and unstructured documents into a set of unified, clean entities and relationships. Midas focuses on data for financial companies and is based on periodic filings with the U.S. Securities and Exchange Commission (SEC) and Federal(More)
SystemML aims at declarative, large-scale machine learning (ML) on top of MapReduce, where high-level ML scripts with R-like syntax are compiled to programs of MR jobs. The declarative specification of ML algorithms enables—in contrast to existing large-scale machine learning libraries— automatic optimization. SystemML's primary focus is on data parallelism(More)
SystemML enables declarative, large-scale machine learning (ML) via a high-level language with R-like syntax. Data scientists use this language to express their ML algorithms with full flexibility but without the need to hand-tune distributed runtime execution plans and system configurations. These ML programs are dynamically compiled and optimized based on(More)