Author
X Xiaopeng, HG Elmongui, X Chai, WG Aref
Contents
Moving objects equipped with locating devices can report
their locations periodically to data stream servers.
With the pervasiveness of moving objects, one single server
cannot support all objects and queries in a wide area. As
a result, multiple spatio-temporal data stream management
systems must be deployed and thus result in a server network.
It is vital for servers in the network to collaborate
in query evaluation. In this paper, we introduce PLACE*,
a distributed spatio-temporal data stream management system
for moving objects. PLACE* supports continuous moving
queries that hop among multiple regional servers. To
minimize the execution cost, a new Query-Track-Participate
(QTP) query processing model is proposed inside PLACE*.
In the QTP model, a query is continuously answered by a
querying server, a tracking server, and a set of participating
servers. In this paper, we focus on distributed query
plan generation, query execution and update algorithms
for answering continuous range queries and continuous k-
Nearest-Neighbor queries in PLACE* using QTP. An extensive
experimental study is presented to demonstrate the effectiveness
of the proposed algorithms on the scalability of PLACE
