Mining Frequent Itemsets over Uncertain Databases

In recent years, due to the wide applications of uncertain data, mining frequent itemsets over uncertain databases has attracted much attention. In uncertain databases, the support of an itemset is a random variable instead of a fixed occurrence counting of this itemset. Thus, unlike the corresponding problem in deterministic databases where the frequent itemset has a unique definition, the frequent itemset under uncertain environments has two different definitions so far. The first definition, referred as the expected support-based frequent itemset, employs the expectation of the support of an itemset to measure whether this itemset is frequent. The second definition, referred as the probabilistic frequent itemset, uses the probability of the support of an itemset to measure its frequency. Thus, existing work on mining frequent itemsets over uncertain databases is divided into two different groups and no study is conducted to comprehensively compare the two different definitions. In addition, since no uniform experimental platform exists, current solutions for the same definition even generate inconsistent results. In this paper, we firstly aim to clarify the relationship between the two different definitions. Through extensive experiments, we verify that the two definitions have a tight connection and can be unified together when the size of data is large enough. Secondly, we provide baseline implementations of eight existing representative algorithms and test their performances with uniform measures fairly. Finally, according to the fair tests over many different benchmark data sets, we clarify several existing inconsistent conclusions and discuss some new findings.Comment: VLDB201

Item-centric mining of frequent patterns from big uncertain data

Item-centric mining of frequent patterns from big uncertain dat

OTrack: Order tracking for luggage in mobile RFID systems

Abstract—In many logistics applications of RFID technology, goods attached with tags are placed on moving conveyor belts for processing. It is important to figure out the order of goods on the belts so that further actions like sorting can be accurately taken on proper goods. Due to arbitrary goods placement or the irregularity of wireless signal propagation, neither of the order of tag identification nor the received signal strength provides sufficient evidence on their relative positions on the belts. In this study, we observe, from experiments, a critical region of reading rate when a tag gets close enough to a reader. This phenomenon, as well as other signal attributes, yields the stable indication of tag order. We establish a probabilistic model for recognizing the transient critical region and propose the OTrack protocol to continuously monitor the order of tags. To validate the protocol, we evaluate the accuracy and effectiveness through a one-month experiment conducted through a working conveyor at Beijing Capital International Airport. I

Efficient Matching of Substrings in Uncertain Sequences

Substring matching is fundamental to data mining methods for se-quential data. It involves checking the existence of a short subse-quence within a longer sequence, ensuring no gaps within a match. Whilst a large amount of existing work has focused on substring matching and mining techniques for certain sequences, there are on-ly a few results for uncertain sequences. Uncertain sequences pro-vide powerful representations for modelling sequence behavioural characteristics in emerging domains, such as bioinformatics, sen-sor streams and trajectory analysis. In this paper, we focus on the core problem of computing substring matching probability in un-certain sequences and propose an efficient dynamic programming algorithm for this task. We demonstrate our approach is both com-petitive theoretically, as well as effective and scalable experimental-ly. Our results contribute towards a foundation for adapting classic sequence mining methods to deal with uncertain data.