PRISWDB is a full-fledged parallel, main memory relational database management system the design of which is characterized by two main ideas. In the first place, high performance is obtained by the use of parallelism for query processing and main memory storage of the entire database. In the second place, a flexible architecture for experimenting with functionality and performance is obtained via a modular implementation of the system in an object-oriented programming language. This paper describes the design and implementation of P R I S W D B in detail. Also, a performance evaluation of the system shows that the system is comparable to other state-ofthe-art database machines. The prototype implementation of the system is ready, and runs on a 100-node parallel multiprocessor. The achieved flexibility of the system makes it a valuable platform for research in various directions.Index Terms-Parallel, main memory, relational database management system, design and implementation, architecture, query execution, experimentation, integrity constraints.

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Trajectories of people contain a vast amount of information on users' interests and popularity of locations. To obtain this information, the places visited by the owner of the device on such a trajectory need to be recognized. However, the location information on a point of interest (POI) in a database is normally limited to an address and a coordinate pair, rather than a polygon describing its boundaries. A region of interest can be used to intersect trajectories to match trajectories with objects of interest. In the absence of expensive and often not publicly available detailed spatial data like cadastral data, we need to approximate this ROI. In this paper, we present several approaches to approximate the size and shape of ROIs, by integrating data from multiple public sources, a validation technique, and a validation of these approaches against the cadastral data of the city of Enschede, The Netherlands.

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In the PRISMA project, a 100-node parallel machine was constructed by Philips Electronics and Dutch academia, along with a parallel main memory database sytem (PRISMA/DB). When this paper was written, the project had just finished (along the with idea of database machines). This paper abstracts from the particular features of PRISMA/DB, and evaluates and analyzes the performance trade-offs for a wide range of parallel query processing strategies. Its clear style of presentation, along with careful attention to previous work both in its discussion as well as in the experiments and analysis, make this paper into a concise introductory or "refereshment" text for researchers interested in parallel query execution. Currently, parallel databases may no longer be in vogue, but this line of work, in particular the non-blocking symmetric hash-join also described here, is nowadays frequently cited in articles on stream query processing and peer-to-peer databases. For me personally, a reminiscence of this paper automatically becomes a reminiscence of its primary author, Annita Wilschut, who is no longer active in our field after health problems. She was the coordinator of the (post-PRISMA) research project containing my Ph.D. track. Her sense of community and willingness to share knowledge, resulted in her teaching me to be a scientist, and enthusistically transmitting basic "database values", so crucial for our research field (e.g., the importance of abstract query languages that speciy "what" not "how", allowing both data independence and query optimization, etc. etc.). Ihab Ilyas, University of Waterloo, ilyas@uwaterloo.ca. [Ronald Fagin, Amnon Lotem and Moni Naor. Optimal aggregation algorithms for middleware. There are many papers that significantly influenced my research; hence, I decided to pick the paper with the most direct impact on my Ph.D. thesis. I became first aware of this paper by attending the best paper award presentation in PODS 2001, Santa Barbara. I still remember the lively presentation of the paper by Ronald Fagin. I was impressed by the fact that the algorithms are extremely intuitive and the optimality results are very strong. I read the paper during the conference and its extended version from Ronalds website, later on. I could immediately see a strong relationship to what I was doing at Purdue; I was working on providing efficient query processing techniques for top-k similarity queries, addressing the inefficiency of current query engines in handling this type of query, which is dominant in many applications. This paper introduced a family of optimal rank-aggregation algorithms for combining multiple ranked lists according to some monotone combining function. The objective was to get the overall top-k objects using the minimum number of accesses to the inputs lists. Reading the paper raised several research questions, one of which was how to leverage these simple and extremely efficient algorithms in relational databases to develop rank-aware query processing. In my Ph.D. thesis, I focused on ge...