Guoqiong Liao

KLEAP: an efficient cleaning method to remove cross-reads in RFID streams

Recently, the RFID technology has been widely used in many kinds of applications. However, because of the interference from environmental factors and limitations of the radio frequency technology, the data streams collected by the RFID readers are usually contain a lot of cross-reads. To address this issue, we propose a KerneL dEnsity-bAsed Probability cleaning method (KLEAP) to remove cross-reads within a sliding window. The method estimates the density of each tag using a kernel-based function. The reader corresponding to the micro-cluster with the largest density will be regarded as the position that the tagged object should locate in current window, and the readings derived from other readers will be treated as the cross-reads. Experiments verify the effectiveness and efficiency of the proposed method.

JUFE at INEX 2011 Snippet Retrieval Track

Jiangxi University of Finance and Economics (JUFE) submitted 8 runs to the Snippet Retrieval Track at INEX 2011.This report describes an XML snippet retrieval method based on Average Topic Generalization (ATG) model used by JUFE. The basic idea of the ATG is that different element in an XML document plays different role and hence should has distinguishing importance. The ATG model sets a weight automatically to each element according to its tag or path in the XML document. Then, the BM25EW model based on the ATG is proposed to retrieve and rank the relevant elements in an XML document collection. All windows in the most relevant elements are scored and those windows with higher scores are extracted as snippets. By comparing with the runs under different strategies, the performance are discussed and analyzed in detail.

Two-Phase Mining for Frequent Closed Episodes

The concept of episodes was introduced for discovering the useful and interesting temporal patterns from the sequential data. Over the years, many episode mining strategies have been suggested, which can be roughly classified into two classes: Apriori-based breadth-first algorithms and projection-based depth-first algorithms. As we know, both kinds of algorithms are level-wise pattern growth methods, so that they have higher computational overhead due to level-wise growth iteration. In addition, their mining time will increase with the increase of sequence length. In the paper, we propose a novel two-phase strategy to discover frequent closed episodes. That is, in phase I, we present a level-wise shrinking mechanism, based on maximal duration episodes, to find the candidate frequent closed episodes from the episodes with the same 2-neighboring episode prefix, and in phase II, we compare the candidates with different prefixes to discover the final frequent closed episodes. The advantage of the suggested mining strategy is it can reduce mining time due to narrowing episode mapping range when doing closure judgment. Experiments on simulated and real datasets demonstrate that the suggested strategy is effective and efficient.

Trajectory Event Cleaning for Mobile RFID Objects

With the rapid development of Radio Frequency Identification (RFID), sensor and wireless technologies, a large amount of trajectory data of moving objects are emerging, and trajectory data mining has received more and more attentions recently. However, since the data collected by sensors and RFID readers are usually noisy, it is necessary and meaningful to clean up the noise, including missing detection events and cross detection events, so as to provide high quality data for various applications using trajectory data. Cleaning up the trajectory events should take into account of uncertainty of location and unreliability of event detection at the same time. In the paper, we first discuss the rules to distinguish between normal detection events and false detection events in the trajectories, using constraints on continuous motion between adjacent detection regions and direct moving time between neighboring physical regions. Then, as a unified cleaning framework, we establish a probabilistic region connection graph to represent region detection features, region connection relationships, and region transition probabilities of neighboring physical regions. Focusing on interpolating missing events, we suggest two path-based probabilistic interpolating strategies, namely, the Most Likely Path (MLP) strategy and the Highest Weighting Probability Path (HWPP) strategy. Also, we discuss pruning rules of candidate paths for reducing computational cost. Finally, we conduct experiments over simulation data to demonstrate the effectiveness and efficiency of the proposed methods.

What happened then and there: Top-k spatio-temporal keyword query

Abstract Social media data, e.g. Tweets, are usually geo-tagged, embedded with creation or posting time, and associated with texts. Nowadays, there is an increasing need for querying such spatio-temporal-text data. In this work, we propose a new type of query, top-k spatio-temporal keyword query (k-STKQ in short), over Twitter-like social media data. A k-STKQ takes a location, a timestamp and a set of keywords as argument, and returns top-k objects that are near the location, close to the timestamp, and relevant to the set of keywords. An example of k-STKQ is to search the tweets mentioning “garage sale” recently sent from some places nearby. The massive amount and dynamic nature of social media data are the primary obstacles towards efficient processing of k-STKQs. In order to return the answers efficiently, we propose a novel index, TiST, for the processing of k-STKQs. TiST partitions the incoming data into subsets, and builds an R-tree index on each subset. The timestamps and texts of the objects are also integrated with the R-trees. To further boost the indexing performance, we propose a routing R-tree based R-tree insertion method, which is inspired by the observation that many sets of objects are similar in their locations. For the texts of objects, we propose a hybrid bitmap-based index, which is space-saving and supports relevance computation. The query processing algorithm is also presented based on the TiST index. We conduct extensive experiments to demonstrate that our solution is capable of providing excellent indexing performance and good query performance.

Probabilistic cleaning over trajectory events of mobile RFID objects

Cleaning up the trajectory events of mobile RFID objects should take the uncertainty of location and unreliability of event detection into account at the same time. In the paper, we first discuss the rules to distinguish false detection events in RFID object trajectories. Then, as a unified cleaning framework, we establish a probabilistic region connection graph to represent region detection features, region connection relationships, and region transition probabilities between neighboring physical regions. Focusing on interpolating the missing events, we suggest two path-based probabilistic cleaning strategies, namely, the Most Likely Path (MLP) strategy and the Highest Weighting Probability Path (HWPP) strategy.