Efficient Processing of Streams of Frequent Itemset Queries
نویسندگان
چکیده
Frequent itemset mining is one of fundamental data mining problems that shares many similarities with traditional database querying. Hence, several query optimization techniques known from database systems have been successfully applied to frequent itemset queries, including reusing results of previous queries and multi-query optimization. In this paper, we consider a new problem of processing of streams of incoming frequent itemset queries, where like in multi-query optimization a number of queries are executed together and share some of their operations, but unlike in previously considered scenarios, new queries are dynamically being added to the currently processed set of queries.
منابع مشابه
Concurrent Processing of Frequent Itemset Queries Using FP-Growth Algorithm
Discovery of frequent itemsets is a very important data mining problem with numerous applications. Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. A significant amount of research on frequent itemset mining has been done so far, focusing mainly on developing faster complete mining al...
متن کاملارائه روشی پویا جهت پاسخ به پرسوجوهای پیوسته تجمّعی اقتضایی
Data Streams are infinite, fast, time-stamp data elements which are received explosively. Generally, these elements need to be processed in an online, real-time way. So, algorithms to process data streams and answer queries on these streams are mostly one-pass. The execution of such algorithms has some challenges such as memory limitation, scheduling, and accuracy of answers. They will be more ...
متن کاملPartition-Based Approach to Processing Batches of Frequent Itemset Queries
We consider the problem of optimizing processing of batches of frequent itemset queries. The problem is a particular case of multiple-query optimization, where the goal is to minimize the total execution time of the set of queries. We propose an algorithm that is a combination of the Mine Merge method, previously proposed for processing of batches of frequent itemset queries, and the Partition ...
متن کاملAn Accelerator for Frequent Itemset Mining from Data Streams with Parallel Item Tree
Frequent itemset mining attempts to find frequent subsets in a transaction database. In this era of big data, demand for frequent itemset mining is increasing. Therefore, the combination of fast implementation and low memory consumption, especially for stream data, is needed. In response to this, we optimize an online algorithm, called Skip LC-SS algorithm [1], for hardware. In this paper, we p...
متن کاملThree Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth
Frequent itemset mining is often regarded as advanced querying where a user specifies the source dataset and pattern constraints using a given constraint model. Recently, a new problem of optimizing processing of sets of frequent itemset queries has been considered and two multiple query optimization techniques for frequent itemset queries: Mine Merge and Common Counting have been proposed and ...
متن کامل