00609nas a2200217 4500008004100000245004900041210004900090490000900139653002000148653002200168653002800190653001900218653002100237653002200258100001100280700001300291700001200304700001700316700001200333856004600345 2010 eng d00aMining Periodic Behaviors for Moving Objects0 aMining Periodic Behaviors for Moving Objects0 v201010aautocorrelation10afourier transform10ahierarchical clustering10amoving objects10aobservation spot10aperiodic behavior1 aLi, Z.1 aDing, B.1 aHan, J.1 aKays, R., W.1 aNye, P. uhttp://dl.acm.org/citation.cfm?id=183594202080nas a2200205 4500008004100000245004500041210004400086300001400130490000900144520156200153653001601715653001801731100001101749700001101760700001501771700001301786700001201799700001701811856004601828 2010 eng d00aMoveMine: Mining Moving Object Databases0 aMoveMine Mining Moving Object Databases a1203-12060 v20103 a

With the maturity of GPS, wireless, and Web technologies, increasing amounts of movement data collected from various moving objects, such as animals, vehicles, mobile devices, and climate radars, have become widely available. Analyzing such data has broad applications, e.g., in ecological study, vehicle control, mobile communication management, and climatological forecast. However, few data mining tools are available for flexible and scalable analysis of massive-scale moving object data. Our system, MoveMine, is designed for sophisticated moving object data mining by integrating several attractive functions including moving object pattern mining and trajectory mining. We explore the state-of-the-art and novel techniques at implementation of the selected functions. A user-friendly interface is provided to facilitate interactive exploration of mining results and flexible tuning of the underlying methods. Since MoveMine is tested on multiple kinds of real data sets, it will benefit users to carry out versatile analysis on these kinds of data. At the same time, it will benefit researchers to realize the importance and limitations of current techniques as well as the potential future studies in moving object data mining.

10adata mining10amovement data1 aLi, Z.1 aji, M.1 aLee, J.-G.1 aTang, L.1 aHan, J.1 aKays, R., W. uhttp://dl.acm.org/citation.cfm?id=180731902017nas a2200169 4500008004100000245005800041210005700099490000900156520153600165653002001701653002701721100001101748700001301759700001201772700001701784856004601801 2010 eng d00aSwarm: Mining Relaxed Temporal Moving Object Clusters0 aSwarm Mining Relaxed Temporal Moving Object Clusters0 v20103 a

Recent improvements in positioning technology make massive moving object data widely available. One important analysis is to find the moving objects that travel together. Existing methods put a strong constraint in defining moving object cluster, that they require the moving objects to stick together for consecutive timestamps. Our key observation is that the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps.

Motivated by this, we propose the concept of swarm which captures the moving objects that move within arbitrary shape of clusters for certain timestamps that are possibly non-consecutive. The goal of our paper is to find all discriminative swarms, namely closed swarm. While the search space for closed swarms is prohibitively huge, we design a method, ObjectGrowth, to efficiently retrieve the answer. In ObjectGrowth, two effective pruning strategies are proposed to greatly reduce the search space and a novel closure checking rule is developed to report closed swarms on-the-fly. Empirical studies on the real data as well as large synthetic data demonstrate the effectiveness and efficiency of our methods.

10aanimal tracking10apositioning technology1 aLi, Z.1 aDing, B.1 aHan, J.1 aKays, R., W. uhttp://dl.acm.org/citation.cfm?id=1920934