Abstract. Moving object environments are characterized by large numbers of objects continuously sending location updates. At times, data arrival rates may spike up, causing the load on the system to exceed its capacity. This may result in increased output latencies, potentially leading to invalid or obsolete answers. Dropping data randomly, the most frequently used approach in the literature for load shedding, may adversely affect the accuracy of the results. We thus propose a load shedding technique customized for spatio-temporal stream data. In our model, spatiotemporal properties, such as location, time, direction and speed over time, serve as critical factors in the load shedding decision. The main idea is to abstract similarly moving objects into moving clusters which serve as summaries of their members ’ movement. Based on resource restrictions, members within clusters may be selectively discarded, while their locations are being approximated by their respective moving clusters. Our experimental study illustrates the performance gains achieved by our load-shedding framework and the tradeoff between the amount of data shed and the result accuracy. 1

Spatio-temporal data streams that are generated from mobile stream sources (e.g., mobile sensors) experience similar environmental conditions that result in distinct phenomena. Several research efforts are dedicated to detect and track various phenomena inside a data stream management system (DSMS). In this paper, we use the detected phenomena to reduce the demand on the DSMS resources. The main idea is to let the query processor observe the input data streams at the phenomena level. Then, each incoming continuous query is directed only to those phenomena that participate in the query answer. Two levels of indexing are employed, a phenomenon index and a query index. The phenomenon index provides a fine resolution view of the input streams that participate in a particular phenomenon. The query index utilizes the phenomenon index to maintain a query deployment map in which each input stream is aware of the set of continuous queries that the stream contributes to their answers. Both indices are updated dynamically in response to the evolving nature of phenomena and to the mobility of the stream sources. Experimental results show the efficiency of this approach with respect to the accuracy of the query result and the resource utilization of the DSMS. 1

Abstract. The volume of real-time streaming data produced by georeferenced sensors and sensor networks is staggeringly large and growing rapidly. Queries on these geo-streams often require tracking spatiotemporal extent (e.g. evolving region) continuously in real time. The notion of real-time monitoring and notification requires support from a database capable of tracking and querying dynamic and transient spatiotemporal events as well as static spatial objects and sending out real-time notifications. In this paper, we leverage the work in data type based spatio-temporal databases and propose new data types called STREAM and their abstract semantics to support geo-stream applications. New operations on STREAM data types are defined and illustrated by embedding them into SQL. 1

Recent advances in large scale data streaming technologies enabled the deploy- ment of a huge number of streaming sources in the surrounding environment, e.g., sensor fields. Streaming sources do not live in isolation. Instead, close-by stream- ing sources experience similar environmental conditions. Hence, close-by streaming sources may indulge in a correlated behavior and generate a “phenomenon”. A phenomenon is characterized by a group of streaming sources that show “similar be- havior” over a period of time. Examples of detectable phenomena include pollution clouds in the air, oil spills at the ocean surface, fire zones in a building, water floods of a river, migration of birds, and epidemic spread of diseases. This dissertation pro- poses a framework to detect, track, and query various forms of phenomena in data streaming environments. This framework empowers data stream management sys- tems (DSMSs) with phenomenon-awareness capabilities. Phenomenon-aware data stream systems use high-level knowledge about phenomena in the data streaming environment to optimize the execution of subsequent user queries. To approach the above goal, this dissertation proposes the principle that “phe- nomenon detection guides query processing” and explores this principle’s implica- tions on DSMSs. Hence, user queries have the option to view the streaming envi- ronment at a higher level, i.e., the phenomenon level. In such a phenomenon-aware query processing paradigm, streams are prioritized and are processed based on a mechanism that tunes query processing towards data streams that contribute to detected phenomena. This dissertation provides a formal definition for a phenomenon, models the phe- nomenon behavior, and proposes an extended syntax that enables the users to reg- ister their interesting phenomenon patterns with the system. Also, this dissertation adopts the concept of phenomenon-aware query processing by adding two major com- ponents to DSMSs: the Phenomenon Detection and Tracking module (PDT-module) and the phenomenon-aware optimizer. The PDT-module encompasses scalable tech- niques to detect the appearance of new phenomena and to track the propagation of already-detected phenomena. The phenomenon-aware optimizer is an adaptive optimizer that optimizes user queries continuously based on the feedback it receives from the PDT-module. Finally, this dissertation considers phenomenon awareness at the distributed setup of sensor networks by providing a phenomenon-aware data ac- quisition protocol and by extending the phenomenon detection process to the sensor- network platform. As a vehicle for this research, the Nile-PhenomenaBase system is prototyped as a framework for phenomenon-aware query processing inside Nile, a data stream management system developed at Purdue University.

The continuous development of wireless networks and mobile devices has motivated an intense research in mobile data services. Some of these services provide the user with context-aware information. Specifically, location-based services and location-dependent queries have attracted a lot of interest. In this article, the existing literature in the field of location-dependent query processing is reviewed. The technological context (mobile computing) and support middleware (such as moving object databases and data stream technology) are described, location-based services and locationdependent queries are defined and classified, and different query processing approaches are reviewed and compared.

We propose a new mechanism, which we term frames, for datadependent windows. In contrast to traditional timestamp-based windows, frames represent just the boundary of a window and can be filled with data from secondary streams or historical data. Examples show how frames can be useful in network and sensor monitoring applications. We present frame definition and implementation in one dimension, discuss extension to multidimensional frames, and identify issues for further investigation.

This article addresses the problem of performing Nearest Neighbor (NN) queries in a Moving Objects Database (MOD) when the trajectories of the objects are uncertain. The answer to an NN query for certain trajectories is time parameterized due to the continuous nature of the motion. However, for uncertain trajectories, even in a single time instant there may be several objects that have a non-zero probability of being a nearest neighbor to a given object. This fact affects the semantics of the answer to an NN query--an effect that is further amplified when the query spans over a time interval. We capture the impact that the uncertainty of the trajectories has on the semantics of the answer to continuous NN queries and we propose a tree structure for representing the answers, along with efficient algorithms to compute them. We also address the issue of performing NN queries when the motion of the objects is restricted to road networks. Finally, we formally define and show how to efficiently execute several variants of continuous NN queries. Our prototype implementation and experiments demonstrate that the proposed algorithms yield significant performance improvements when compared with the corresponding naive approaches. This is a full version of the article that was peer-reviewed and accepted for publication in the VLDB Journal, special issue on Data Management for Mobile Applications, 2011. It includes the final modifications based on the reviewers ’ comments accompanying the acceptance note. However, upon finalizing the camera-ready copy, we were told that the space limit is 25 pages, and we had to reduce some material, which we are making available here.

The continuous development of wireless networks and mobile devices has motivated an intense research in mobile data services. Some of these services provide the user with context-aware information. Specifically, location-based services and location-dependent queries have attracted a lot of interest. In this article, the existing literature in the field of location-dependent query processing is reviewed. The technological context (mobile computing) and support middleware (such as moving object databases and data stream technology) are described, location-based services and locationdependent queries are defined and classified, and different query processing approaches are reviewed and compared.

... systems, two key measures of quality of service (QoS) are: freshness and accuracy. To achieve freshness, the CQ server must perform frequent query re-evaluations. To attain accuracy, the CQ server must receive and process frequent position updates from the mobile nodes. However, it is often difficult to obtain fresh and accurate CQ results simultaneously, due to (a) limited resources in computing and communication and (b) fast-changing load conditions caused by continuous mobile node movement. Hence, a key challenge for a mobile CQ system is: How do we achieve the highest possible quality of the CQ results, in both freshness and accuracy, with currently available resources? In this paper, we formulate this problem as a load shedding one, and develop MobiQual − a QoS-aware approach to performing both update load shedding and query load shedding. The design of MobiQual highlights three important features. (1) Differentiated load shedding: We apply different amounts of query load shedding and update load shedding to different groups of queries and mobile nodes, respectively. (2) Per-query QoS specification: Individualized QoS specifications are used to maximize the overall freshness and accuracy of the query results. (3) Low-cost adaptation: MobiQual dynamically adapts, with a minimal overhead, to changing load conditions and available resources. We conduct a set of comprehensive experiments to evaluate the effectiveness of MobiQual. The results show that, through a careful combination of update and query load shedding, the MobiQual approach leads to much higher freshness and accuracy in the query results in all cases, compared to existing approaches that lack the QoSawareness properties of MobiQual, as well as the solutions that perform query-only or update-only load shedding.