In this paper, we present a new privacy-aware query processing framework Capser * in which mobile and stationary users can obtain snapshot and/or continuous location-based services without revealing their private location information. In particular, we propose a privacy-aware query processor embedded inside a location-based database server to deal with snapshot and continuous queries based on the knowledge of the user’s cloaked location rather than the exact location. Our proposed privacy-aware query processor is completely independent of how we compute the user’s cloaked location. In other words, any existing location anonymization algorithms that blur the user’s private location into cloaked rectilinear areas can be employed to protect the user’s location privacy. We first propose a privacy-aware query processor that not only supports three new privacy-aware query types, but it also achieves a trade-off between query processing cost and answer optimality. Then, to improve system scalability of processing continuous privacy-aware queries, we propose a shared execution paradigm that shares query processing among a large number of continuous queries. The proposed scalable paradigm can be tuned through two parameters to trade off between system scalability and answer optimality. Experimental results show that our query processor achieves high quality snapshot and continuous location-based services while

Many location-based applications require constant monitoring of k-nearest neighbor (k-NN) queries over moving objects within a geographic area. Existing approaches to this problem have focused on predictive queries, and relied on the assumption that the trajectories of the objects are fully predictable at query processing time. We relax this

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With the proliferation of wireless communications and geo-positioning, e-services are envisioned that exploit the positions of a set of continuously moving users to provide context-aware functionality to each individual user. Because advances in disk capacities continue to outperform Moore's Law, it becomes increasingly feasible to store on-line all the position information obtained from the moving e-service users. With the much slower advances in I/O speeds and many concurrent users, indexing techniques are of essence in this scenario. Past

The tremendous increase of cellular phones, GPS-like devices, and RFIDs results in highly dynamic environments where objects as well as queries are continuously moving. In this paper, we present a continuous query processor designed specifically for highly dynamic environments (e.g., location-aware environments). We implemented the proposed continuous query processor inside the PLACE server (Pervasive Location-Aware Computing Environments); a scalable location-aware database server currently developed at Purdue University. The PLACE server extends data streaming management systems to support location-aware environments. Such environments are characterized by the wide variety of continuous spatio-temporal queries and the unbounded spatio-temporal streams. The proposed continuous query processor mainly includes: (1) Developing new incremental spatio-temporal operators to support a wide variety of continuous spatio-temporal queries, (2) Extending the semantic of sliding window queries to deal with spatial sliding windows as well as temporal sliding windows, and (3) Providing a shared execution framework for scalable execution of a set of concurrent continuous spatio-temporal queries. Preliminary experimental evaluation shows the promising performance of the continuous query processor of the PLACE server.

For additional information, see the DB TECH REPORTS homepage: 〈www.cs.aau.dk/DBTR〉. Any software made available via DB TECH REPORTS is provided “as is ” and without any express or implied warranties, including, without limitation, the implied warranty of merchantability and fitness for a particular purpose. The DB TECH REPORTS icon is made from two letters in an early version of the Rune alphabet, which was used by the Vikings, among others. Runes have angular shapes and lack horizontal lines because the primary storage medium was wood, although they may also be found on jewelry, tools, and weapons. Runes were perceived as having magic, hidden powers. The first letter in the logo is “Dagaz, ” the rune for day or daylight and the phonetic equivalent of “d. ” Its meanings include happiness, activity, and satisfaction. The second letter is “Berkano, ” which is associated with the birch tree. Its divinatory meanings include health, new beginnings, growth, plenty, and clearance. It is associated with Idun, goddess of Spring, and with fertility. It is the phonetic equivalent of “b.” Motivated by the increasing need to handle complex, dynamic, uncertain multidimensional data in location-based warehouses, this paper proposes a novel probabilistic data model that can address the complexities of such data. The model provides a foundation for handling complex hierarchical and uncertain data, e.g., data from the location-based services domain such as transportation infrastructures and the attached static and dynamic content such as speed limits and vehicle positions. The paper also presents algebraic operators that support querying of such data. Use of pre-aggregation for implementation of the operators is also discussed. The work is motivated with a real-world case study, based on our collaboration with a leading Danish vendor of location-based services.

Computer Science Geospatial image data obtained by satellites and aircraft are increasingly important to a wide range of applications, such as disaster management, climatol-ogy, and environmental monitoring. Spatio-temporal aggregations are some of the most important operations over such data. Because of the size of the data and the speed at which it is generated, computing such aggregates over geospatial image data is extremely demanding. Due to the special characteristics of the data, existing spatio-temporal aggregation models and evaluation approaches are not suitable for computing aggregates over such data. In this thesis, we analyze the characteristics of streaming geospatial image data and outline the key challenges of spatio-temporal aggregate computations. By showing that traditional aggregation models do not always provide an accurate view of the data, we propose new spatio-temporal aggregation models that infuse a more meaningful semantics into a query. More importantly, our experiments show that ex-isting approaches do not evaluate these queries efficiently. Existing approaches do not

The OSS (Open Source Software) phenomenon is a novel, widely growing approach to develop both applications and infrastructure software recently. The fast growth of the community increases the interests in OSS related research. Accurate prediction of the project success is one of the interesting studies in OSS research. We propose to use the ST (Spatial Temporal) data mining techniques to predict the project success in the OSS community. ST mining has been studied in Euclidean distance based spatial systems like GIS, but to date has only received little attention in non-Euclidean network structured evolving system like the OSS community. In this paper, we introduce novel methods to project the evolving OSS community in a spatio-temporal data set and related ST mining algorithms to process the data set. Using ST mining techniques we propose, we are able to get the prediction of project success in the OSS community. We also present a detailed analysis and experimentally demonstrate the effectiveness and efficiency of these techniques in a real OSS community -- SourceForge.net. The results show that our techniques can predict the project success and they are also useful in other non-Euclidean spatial systems.

The collection of historical or real-time data on moving objects is quickly becoming a ubiquitous task. With the help of GPS devices, RFID sensors, RADAR, satellites, and other technologies, mobile objects of all sizes, whether it be a tiny cellphone or a giant ocean liner, can be easily tracked around the globe. Many fundamental problems in the database field have found their parallels in the moving object domain. They include indexing and query processing of moving objects over static or continuous queries and similarity search between moving objects. The same has happened with data mining problems as well. Clustering of moving objects is one popular topic; spatial association patterns is another. However, even with the recent attention, there are still many unexplored areas in moving objects research. Specifically, higher semantic level problems remain mostly untouched. One example is anomaly detection. With the ever-increasing focus on video surveillance, many cities are tracking and analyzing vehicles as they move throughout the city. With the ultimate goal of automated reporting and alerting, sophisticated algorithms are needed to evaluate the moving object trajectories. Furthermore, associations with other multi-dimensional features will need to be considered as well. Another example is periodic traffic pattern detection. Everyone

Many database applications deal with spatio-temporal phenomena, and during the last decade a lot of research targeted locationbased services, moving objects, traffic jam preventions, meteorology, etc. In strong contrast, there exist only very few proposals for an implementation of a spatio-temporal database system let alone a web-based spatio-temporal information system. This paper describes the design and implementation of a webbased spatio-temporal information system. The system uses Secondo as spatio-temporal DBMS for handling moving objects and MapServer as an OGC-compliant rendering engine for static spatial data. We describe the architecture of the system and compare our system with a standalone application. The paper investigates in detail issues that arise in the context of the web. First, we describe an implementation of a lightweight client that takes advantage of the functionality offered by Secondo and MapServer. Second, we describe how moving objects can be represented in GML. We discuss possible GML representations, propose an extension of GML that uses 3D segments (2D location + time) to represent moving objects, and present experiments that compare the solutions.