This paper introduces the Scalable INcremental hash-based Algorithm (SINA, for short); a new algorithm for evaluating a set of concurrent continuous spatio-temporal queries. SINA is designed with two goals in mind: (1) Scalability in terms of the number of concurrent continuous spatiotemporal queries, and (2) Incremental evaluation of continuous spatio-temporal queries. SINA achieves scalability by employing a shared execution paradigm where the execution of continuous spatio-temporal queries is abstracted as a spatial join between a set of moving objects and a set of moving queries. Incremental evaluation is achieved by computing only the updates of the previously reported answer. We introduce two types of updates, namely positive and negative updates. Positive or negative updates indicate that a certain object should be added to or removed from the previously reported answer, respectively. SINA manages the computation of positive and negative updates via three phases: the hashing phase, the invalidation phase, and the joining phase. The hashing phase employs an in-memory hash-based join algorithm that results in a set of positive updates. The invalidation phase is triggered every T seconds or when the memory is fully occupied to produce a set of negative updates. Finally, the joining phase is triggered by the end of the invalidation phase to produce a set of both positive and negative updates that result from joining in-memory data with in-disk data. Experimental results show that SINA is scalable and is more e#cient than other index-based spatio-temporal algorithms.

Location-aware environments are characterized by a large number of objects and a large number of continuous queries. Both the objects and continuous queries may change their locations over time. In this paper, we focus on continuous k-nearest neighbor queries (CKNN, for short). We present a new algorithm, termed SEA-CNN, for answering continuously a collection of concurrent CKNN queries. SEA-CNN has two important features: incremental evaluation and shared execution. SEA-CNN achieves both efficiency and scalability in the presence of a set of concurrent queries. Furthermore, SEA-CNN does not make any assumptions about the movement of objects, e.g., the objects velocities and shapes of trajectories, or about the mutability of the objects and/or the queries, i.e., moving or stationary queries issued on moving or stationary objects. We provide theoretical analysis of SEA-CNN with respect to the execution costs, memory requirements and effects of tunable parameters. Comprehensive experimentation shows that SEA-CNN is highly scalable and is more efficient in terms of both I/O and CPU costs in comparison to other R-tree-based CKNN techniques. 1.

Spatial database research has continued to advance greatly since three decades ago, addressing the growing data management and analysis needs of spatial applications. This research has produced a taxonomy of models for space, conceptual models, spatial query languages and query processing, spatial file organization and indexes, and spatial data mining. However, emerging needs for spatial database systems include the handling of 3D spatial data, temporal dimension with spatial data, and spatial data visualization. In addition, the rise of new systems such as sensor networks and multi-core processors is likely to have an impact in spatial databases. The goal of this paper is to provide a broad overview of the recent advancements in spatial databases and research needs in each area.

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.

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.