ΠΜΣ «Προηγμένα Συστήματα Πληροφορικής» Τμήμα Πληροφορικής, Πανεπιστήμιο Πειραιώς. ΠΡΟΗΓΜΕΝΑ ΘΕΜΑΤΑ ΒΑΣΕΩΝ ΔΕΔΟΜΕΝΩΝ (διδάσκων: Γιάννης Θεοδωρίδης)

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1 ΠΜΣ «Προηγμένα Συστήματα Πληροφορικής» Τμήμα Πληροφορικής, Πανεπιστήμιο Πειραιώς Ακαδημαϊκό έτος ΠΡΟΗΓΜΕΝΑ ΘΕΜΑΤΑ ΒΑΣΕΩΝ ΔΕΔΟΜΕΝΩΝ (διδάσκων: Γιάννης Θεοδωρίδης) ΒΙΒΛΙΟΓΡΑΦΙΚΗ ΕΡΓΑΣΙΑ ΕΞΑΜΗΝΟΥ ομάδες των 2 3 ατόμων Αντικείμενο της άσκησης Σκοπός της άσκησης είναι η εμβάθυνση σε θεωρητικά θέματα Βάσεων Δεδομένων. Κάθε ομάδα θα επιλέξει ένα θέμα (από τα παρακάτω ενδεικτικά θέματα ή που θα προτείνει στο διδάσκοντα) και, αφού διαβάσει καλά τη σχετική βιβλιογραφία, θα αναπτύξει τη συγκεκριμένη ερευνητική περιοχή σε μια έκθεση (report) συνοδευόμενη από μια συνοπτική παρουσίαση (presentation). Στόχος είναι η κατανόηση της περιοχής και όχι η αποστήθιση / αντιγραφή υλικού που υπάρχει στο Internet. Παραδοτέο Το παραδοτέο αποτελείται από τεχνική αναφορά (technical report) και παρουσίαση (presentation) σε έντυπη και ηλεκτρονική μορφή. Η μορφοποίηση της τεχνικής αναφοράς θα πρέπει να γίνει σύμφωνα με τις οδηγίες που δίνει ο εκδοτικός οίκος Springer (συγκεκριμένα, σύμφωνα με το πρότυπο μορφοποίησης για εργασίες σε πρακτικά και συλλογικούς τόμους proceedings and other multiauthor volumes) 1. Τελική ημερομηνία παράδοσης: 15 Δεκεμβρίου 2008 στη θυρίδα του κ. Θεοδωρίδη (γραφείο 501). Ενδεχομένως θα γίνει παρουσίαση της δουλειάς, είτε στα πλαίσια του μαθήματος (σε μια από τις τελευταίες διαλέξεις) είτε στα πλαίσια της συνάντησης του Εργαστηρίου InfoLab. 1 Οι οδηγίες είναι διαθέσιμες στην ιστοσελίδα SGWID=

2 Απορίες σχετικά με την άσκηση Για οποιαδήποτε απορία σχετικά με την άσκηση μπορείτε να απευθύνεστε στον διδάσκοντα, είτε μέσω είτε μέσω του σχετικού νήματος στο forum του μαθήματος. Όλες οι δημοσιεύσεις που αναφέρονται μπορούν να βρεθούν μέσα από το DBLP Computer Science Bibliography ( trier.de/). Σε περίπτωση δυσκολίας να εντοπίσετε το υλικό, μπορείτε να απευθυνθείτε στον διδάσκοντα. Ενδεικτικά θέματα 1. Ranking (top k, skyline) Queries. Due to novel application requirements, the need for processing ranking queries has emerged. Examples include top k selection queries, top k join queries, skyline queries. For example, skyline is defined as follows: A point p of a multidimensional dataset D is said to dominate another point q of D if p is better than q in at least one dimension and not worse than q in all other dimensions for a defined preference function. The skyline of a dataset D, also called the set of maximal vectors or the Pareto set, is the set of all points s contained in D that are not dominated by any other elements of D. Stephan Börzsönyi, Donald Kossmann, Konrad Stocker: The Skyline Operator. ICDE 2001: Ihab F. Ilyas, Walid G. Aref, Ahmed K. Elmagarmid: Supporting top k join queries in relational databases. VLDB J. 13(3): (2004) Ihab F. Ilyas, Walid G. Aref: Rank aware query processing and optimization. Tutorial notes, ICDE 2005 Dimitris Papadias, Yufei Tao, Greg Fu, Bernhard Seeger: Progressive skyline computation in database systems. ACM Trans. Database Syst. 30(1): (2005) Chengkai Li, Kevin Chen Chuan Chang, Ihab F. Ilyas, Sumin Song: RankSQL: Query Algebra and Optimization for Relational Top k Queries. SIGMOD Conference 2005: Chengkai Li, Mohamed A. Soliman, Kevin Chen Chuan Chang, Ihab F. Ilyas: RankSQL: Supporting Ranking Queries in Relational Database Management Systems. VLDB 2005: Nikos Mamoulis, Man Lung Yiu, Kit Hung Cheng, David W. Cheung: Efficient top k aggregation of ranked inputs. ACM Trans. Database Syst. 32(3): 19 (2007) Πιθανό ερευνητικό θέμα για περαιτέρω εμβάθυνση: Line of Sight (LOS) algorithms for skyline query processing (στην περίπτωση αυτή, είναι απαραίτητη η παρακολούθηση του μαθήματος του 2 ου εξαμήνου: «Συστήματα Γεωγραφικών Πληροφοριών») 2. Location based Query Processing in Spatial Network Databases. With the development of positioning technologies such as the Global Positioning System (GPS), many applications are developed in transportation domains by taking advantages of monitoring object movements in traffic (e.g., road) networks where the position and 2

3 distance of objects are constrained by spatial networks. Two important types of these queries, widely used in location based services, traffic monitoring and emergency management, include the range query and the k nearest neighbor (k NN) query. Papadias, D., Zhang, J., Mamoulis, N., and Tao, Y. (2003) Query Processing in Spatial Network Databases. Proc. VLDB 03. Jensen, C. S., Kolarvr, J., Pedersen, T. B., and Timko, I. (2003) Nearest Neighbor Queries in Road Networks. Proc. ACM GIS 03. Cho, H. J. and Chung, C. W. (2005) An Efficient and Scalable Approach to CNN Queries in a Road Network. Proc. VLDB 05. Πιθανό ερευνητικό θέμα για περαιτέρω εμβάθυνση: Trip planning queries (στην περίπτωση αυτή, είναι απαραίτητη η παρακολούθηση του μαθήματος του 2 ου εξαμήνου: «Συστήματα Γεωγραφικών Πληροφοριών») 3. Probabilistic Queries over Uncertain / Imprecise Data. Problems related to data uncertainty / imprecision management have recently attracted research interest. Several researchers have studied the classification and evaluation of probabilistic queries while others have studied indexing and query processing issues. Recent works investigate efficient retrieval methods of top k queries over uncertain databases. Cheng, R., Kalashnikov, D., and Prabhakar, S. (2003) Evaluating Probabilistic Queries of Imprecise Data. Proc. ACM SIGMOD 03. Tao, Y. et al. (2005) Indexing multi dimensional uncertain data with arbitrary probability density functions. Proc. VLDB 05. Re, C., Dalvi, N., and Suciu, D. (2007) Efficient top k query evaluation on probabilistic data. Proc. ICDE 07. Suciu, D. (2007) Techniques for managing probabilistic data. Notes in EDBT summer school. URL: EDBT SS 07.pdf. Πιθανό ερευνητικό θέμα για περαιτέρω εμβάθυνση: Discovering borders in imprecise spatial datasets (στην περίπτωση αυτή, είναι απαραίτητη η παρακολούθηση του μαθήματος του 2 ου εξαμήνου: «Συστήματα Γεωγραφικών Πληροφοριών») 4. RFID databases An Overview. Radio Frequency Identification (RFID) applications set to play an essential role in object tracking and supply chain management systems. In the near future, it is expected that every major retailer will use RFID systems to track movement of products from suppliers to warehouses, backrooms and eventually to points of sale. The volume information generated by such systems can be enormous as each individual item (a pallet, a case, or an SKU) will leave trail of data as it moves through different locations. Recent works investigate methods for the management and exploitation of this vast amount of data. Agrawal, R., Cheung, A., Kailing, K., and Schonauer, S. (2006) Towards Traceability across Sovereign, Distributed RFID Databases. Proc. IDEAS 06. 3

4 Gonzalez, H., Han, J., Li, X., and Klabjan, D. (2006) Warehousing and Analyzing Massive RFID Data Sets. Proc. ICDE 06. Gonzalez, H., Han, J., and Li, X. (2006) Flowcube: constructing RFID flowcubes for multi dimensional analysis of commodity flows. Proc. VLDB 06. Cheung, A., Kailing, K., and Schönauer, S. (2007) Theseos: A Query Engine for Traceability across Sovereign, Distributed RFID Databases, demo, Proc. ICDE ʹ07. Sheng, Q. Z., Li, X., and Zeadally, S. (2008) Enabling Next Generation RFID Applications: Solutions and Challenges. IEEE Computer, 41(9):21 28, September 2008, Πιθανό ερευνητικό θέμα για περαιτέρω εμβάθυνση: RFID data warehouses issues and solutions (στην περίπτωση αυτή, είναι απαραίτητη η παρακολούθηση του μαθήματος του 2 ου εξαμήνου: «Αποθήκες Δεδομένων και Εξόρυξη Γνώσης») 5. Query Processing in Data Streams. Applications that deal with continuous data streams are becoming increasingly important. Many stream based applications, such as environmental monitoring, surveillance, tracking, plant maintenance, and telecommunications data management, require the ability to handle huge volumes of continuous data streams arriving in real time; to be able to function efficiently under uncertainty in data, and to be able to deliver results in a timely fashion. Traditional database management systems are inherently ill equipped for supporting such tasks, mainly because they are designed based on the implicit assumption that the system is a passive data repository storing a large but finite collection of data elements, which are processed in response to human initiated queries. Moreover, in many of these applications, there is a need to ask questions that require comparing and combining stored, historical data with real time streaming data. Several stream management systems have been proposed that deal with the above mentioned challenges and the goal of this work is to provide an overview of these systems. Several aspects of data management need to be reconsidered in the presence of data streams. This work focus on how to define and evaluate continuous queries over data streams, which are queries that are issued once and then logically run continuously over the database (in contrast to traditional one time queries which are run once to completion over the current data sets). Babcock, B., Babu, S., Datar, M., Motwani, R., and Widom, J. (2002) Models and issues in data stream systems. Proc. PODSʹ02 Carney, D., Cetintemel, U., Cherniack, M., Convey, C., Lee, S., Seidman, G., Stonebraker, M., Tatbul, N., and Zdonik, S. (2002) Monitoring Streams A New Class of Data Management Applications. Proc. VLDB 02. Babu, S. and Widom, J Continuous queries over data streams. SIGMOD Rec. 30, 3 (Sep. 2001), G. Cormode and M. Garofalakis. Streaming in a Connected World: Querying and Tracking Distributed Data Streams. Proceedings of SIGMOD, H. S. Lim, J. G. Lee, M. J. Lee, K. Y. Whang, I. Y. Song: Continuous query processing in data streams using duality of data and queries. Proceedings of SIGMOD, M. F. Mokbel, X. Xiong, M. A. Hammad, W. G. Aref: Continuous Query Processing of Spatio Temporal Data Streams in PLACE. GeoInformatica 9(4): (2005) 4

5 Πιθανό ερευνητικό θέμα για περαιτέρω εμβάθυνση: Sketches for data stream summarization 6. In network query processing and data aggregation in wireless sensor networks. Wireless sensor networks are becoming popular in many environmental, industrial, military and other application areas. Individual sensor nodes with limited capabilities constitute inexpensive, disposable devices that are scattered in, often harsh, environments of interest so as to collect, process and communicate desired measurements of physical quantities. Despite the limitations enforced by the design and nature of their primitives, sensor networks serve as a powerful tool in many monitoring and control applications due to synergy effects. In order to maximize the benefit from sensor use, one of our major goals is to minimize power consumption which implies prolonged network lifetime. The power consumption of each sensor node tends to be dominated by the cost of transmitting and receiving messages (data). As a result, we aim at primarily reducing communication costs (by reducing the amount of data to be transmitted) to the best possible degree. To achieve that In network query processing and data aggregation techniques are widely used to save energy, increase scalability, and reduce computation. There have been a number of research studies pursuing efficient innetwork aggregation in the literature. Madden, S.R., Franklin, M.J., Hellerstein, J.M., and Hong, W. (2005) TinyDB: An Acquisitional Query Processing System for Sensor Networks. ACM Transactions on Database Systems, 30(1), March Yoon, S. and Shahabi, C. (2007) The Clustered AGgregation (CAG) Technique Leveraging Spatial and Temporal Correlations in Wireless Sensor Networks. ACM Transactions on Sensor Networks, 3(1), March Πιθανό ερευνητικό θέμα για περαιτέρω εμβάθυνση: Programming ad hoc sensor networks 7. Data Privacy. Consider a dataset that contains information about e.g. items bought by individuals. Even after removing personal information of the buyer, which can serve as link to his identity, the publication of such data is still subject to privacy attacks from adversaries who have partial knowledge about the dataset. Several privacy preserving (especially, k anonymization) techniques have been proposed to address this issue. (special issue) Gagan Aggarwal, Tomás Feder, Krishnaram Kenthapadi, Samir Khuller, Rina Panigrahy, Dilys Thomas, An Zhu: Achieving anonymity via clustering. PODS 2006: Rakesh Agrawal, Ramakrishnan Srikant, Dilys Thomas: Privacy Preserving OLAP. SIGMOD Conference 2005: Roberto J. Bayardo Jr., Rakesh Agrawal: Data Privacy through Optimal k Anonymization. ICDE 2005: Kristen LeFevre, David J. DeWitt, Raghu Ramakrishnan: Incognito: Efficient Full Domain K Anonymity. SIGMOD Conference 2005:

6 Mehmet Ercan Nergiz, Chris Clifton: Thoughts on k anonymization. Data Knowl. Eng. 63(3): (2007) Πιθανό ερευνητικό θέμα για περαιτέρω εμβάθυνση: Privacy preserving ad hoc OLAP (στην περίπτωση αυτή, είναι απαραίτητη η παρακολούθηση του μαθήματος του 2 ου εξαμήνου: «Αποθήκες Δεδομένων και Εξόρυξη Γνώσης») 8. Peer To Peer Data Management H. Cao, O. Wolfson, B. Xu and H. Yin (2005) MOBI DIC: MOBIle DIscovery of local Resources in Peer to Peer Wireless Network. IEEE Data Engineering Bulletin, 28(3): Georgia Koloniari, Evaggelia Pitoura: Peer to peer management of XML data: issues and research challenges. SIGMOD Record 34(2): 6 17 (2005) Y. Luo, O. Wolfson, B. Xu (2007) A Spatial Temporal Approach for Selective Data Dissemination in Mobile Peer to peer Networks. Proc. ICWMC 07. B. Xu and O. Wolfson (2004) Data Management in Mobile Peer to Peer Networks. Proc. DBISP2Pʹ Indexing and Searching in Metric Spaces. Tolga Bozkaya, Z. Meral Özsoyoglu: Indexing Large Metric Spaces for Similarity Search Queries. ACM Trans. Database Syst. 24(3): (1999) Edgar Chávez, Gonzalo Navarro, Ricardo A. Baeza Yates, José L. Marroquín: Searching in metric spaces. ACM Comput. Surv. 33(3): (2001) Paolo Ciaccia, Marco Patella, Pavel Zezula: M tree: An Efficient Access Method for Similarity Search in Metric Spaces. VLDB 1997: Gísli R. Hjaltason, Hanan Samet: Index driven similarity search in metric spaces. ACM Trans. Database Syst. 28(4): (2003) Caetano Traina Jr., Agma J. M. Traina, Christos Faloutsos, Bernhard Seeger: Fast Indexing and Visualization of Metric Data Sets using Slim Trees. IEEE Trans. Knowl. Data Eng. 14(2): (2002) 10. Data Quality. (special issue) 11. Data Management in Social Networks. (special issue) (special issue) 6