Final exam and assignament of the course on advanced database systems, 2013-2014

Grading
Grades are based on a final exam (%) and a final assignament.

1. To study a research paper.
2. To carry out a small project which includes an experimental part.

The final assignament can be carried in groups of up to 2 students. While the students are carrying out the assignament, the teacher is availble for hints and to verify the preliminary versions of the assignament.

1. Research papers
   Students must produce a short presentation with slides (10-15 minutes) of a research manuscript (obviously, the presentation must be autonomously produced and cannot be copied from any existing one).
   • Web spam detection is the problem of recognizing pages that contain spam. In [1], a survey on spam detection methods is contained. The two papers [2], [3] domenstrate the importance of using the web connectivity to detect the spam.
   • Learning to rank is the application of machine learning to the constructions of ranking models for information retrieval systems. The work [8] provides a general description of how such a problem can be faced in search engines. Several tutorials about learning to rank are available here. Three of the most well known methods are: RankNet [4] (which is said to be used in Microsoft's Bing), AdaRank [5] and ListNet [6]. Finally, the method [9] has been develop at our department.
   • Text categorization is the problem of classifying a text according to the content. A survey on the methods used for text categorization is in [12]. The most common approaches are based on naive bayes, f.i. [10] and on support vetor machines, f.i. [11].
   • Adertising on the web allows to deliver to each user a customized advertise. However, associating users and advertises is a difficult and resource consuming task. An introduction to this topic is in [19].
   • Predicting user behaviours allow to implement tools that help the users by suggesting them products and, more generally, usueful information. These tools are called recommender systems and have been widely used n the web. In [20], such a topic is introduced.
   • Detecting frauds is a crucial activity for several companies and organizations, f.i. insurance companies, telephone companies, banks. Fraud detection is a difficult task, since fraudulent behaviours are often generated by anomalous activities, for which a sufficient number of examples is not available. Fraud detection techniques are reveiwed in [13] and [14].
   • Medical informatics is a field, in which data mining techniques play an important role. A wide variety of applications are encountered in medical informatics. A survey of those applications is in [15].
   • Bioinformatics is the application of information technology to the field of molecular biology. Such a field is characterized by huge biological datasets, whose analysis involve adavnaced data amining tools using techniques from databases, statistics, computer scicence and machine learning. Chapter [16] includes a survey on this topic.
   • Modelling customer bahaviour in the future is very important for customer relationship management, (e.g. to manage call centers, to improve marketing and sale efforts). Customer behaviour can be modelled both by exploiting past behaviours and demografic information. A survey on this topic is in [17].
   • The goal of feature selection methods is to reduce the domain dimension in data mining applications, by distinguishing important features from non-impontant ones. Actually, data mining techniques (f.i., methods or classification, clustering and associaiton rule extraction) often must cope with domains containing a large number of features, most of which are not useful to solve the problem at hand. An introduction to this topic is in [21].
   • Deep neural networks are a particular class of articial neural networks characterized by beeing composed of several hidden layers. Recently, they have been applied on several problems, so as image classiication [22,23] and speech recognition [24]: the success has been so evident that several companies, including Google, Microsft and Facebook, immediately started to use such technology in their products.
2. Experimental projects
   The following one is a list of examples of experimental projects. Such a list will be modified in the future on the base of project assignments and when new benchmarks will become available. Students can propose their own projects, which can be also correlated to a project in another course or to an internship. Students should discuss in advance the project with the theacher. If the project is particularly demanding, the student may be granted a reduction on the topics to study for the oral examination. Students must present the teacher the results of their experimental actvity, by a short slide presentation or by showing directly the results produced by the software.
   • There are several benchmarks and competitions for comparing data mining techniques, for example, datasets for data mining at University of Edimburg, competitions of TREK conferences. Facing those benchmarks may be demanding, so you need to discuss your choice with the theacher before going to work on it.
   • WEBSPAM-UK2006 is a benchmark that has been used in one of the competitions whose goal was to compare web spam detection algorithms (see this site for information about the competition). The dataset contains a snapshot of web containg with 18 million of pages extracted from 11.400 hosts. Such a benchmark can be used to experiment spam detection methods.
   • LETOR has been extensively used to test query ranking algorithms. LETOR includes several benchmarks, each one containinig information about a set of queries and a set of documents. The purpose is to design algorithms that are able to sort the documents according to the relevance to the queries.

• [1] Spirin, N., & Han, J. (2012). Survey on web spam detection: principles and algorithms. ACM SIGKDD Explorations Newsletter, 13(2), 50-64.
• [2] Castillo, C., Donato, D., Gionis, A., Murdock, V., & Silvestri, F. (2007, July). Know your neighbors: Web spam detection using the web topology. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 423-430). ACM.
• [3] Becchetti, L., Castillo, C., Donato, D., Baeza-Yates, R., & Leonardi, S. (2008). Link analysis for web spam detection. ACM Transactions on the Web (TWEB), 2(1), 2.
• [4] Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005, August). Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning (pp. 89-96). ACM.
• [5] Xu, J., & Li, H. (2007, July). Adarank: a boosting algorithm for information retrieval. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (pp. 391-398). ACM.
• [6] Cao, Z., Qin, T., Liu, T. Y., Tsai, M. F., & Li, H. (2007, June). Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning (pp. 129-136). ACM..
• [7] Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.
• [8] Joachims, T., & Radlinski, F. (2007). Search engines that learn from implicit feedback. IEEE Computer, 40(8), 34-40.
• [9] Rigutini, L., Papini, T., Maggini, M., & Scarselli, F. (2011). SortNet: Learning to rank by a neural preference function. Neural Networks, IEEE Transactions on, 22(9), 1368-1380.
• [10] McCallum, A., & Nigam, K. (1998, July). A comparison of event models for naive bayes text classification. In AAAI-98 workshop on learning for text categorization (Vol. 752, pp. 41-48).
• [11] Drucker, H., Wu, S., & Vapnik, V. N. (1999). Support vector machines for spam categorization. Neural Networks, IEEE Transactions on, 10(5), 1048-1054.
• [12] Sebastiani, Fabrizio. "Machine learning in automated text categorization." ACM computing surveys (CSUR) 34.1 (2002): 1-47.
• [13] Nisbet R., Elder J., Miner (2009). Handbook of Statistical Analysis and Data Mining Applications Chapter 17: Fraud detection. The book is available from DIISM's library and it is available online from DIISM's network.
• [14] Phua, Clifton, et al.(2010). "A comprehensive survey of data mining-based fraud detection research." arXiv preprint arXiv:1009.6119 .
• [15] Nisbet R., Elder J., Miner (2009). Handbook of Statistical Analysis and Data Mining Applications Chapter 15: Medical informatics. The book is available from DIISM's library and it is available online from DIISM's network.
• [16] Nisbet R., Elder J., Miner (2009). Handbook of Statistical Analysis and Data Mining Applications Chapter 16: Bioinformatics. The book is available from DIISM's library and it is available online from DIISM's network.
• [17] Nisbet R., Elder J., Miner (2009). Handbook of Statistical Analysis and Data Mining Applications Chapter 16: Customer response modelling. The book is available from DIISM's library and it is available online from DIISM's network.
• [19] Leskovec, J., Rajaraman, A., Ullman J. (2014). Mining of massive datasets. Chapter 8.
• [20] Leskovec, J., Rajaraman, A., Ullman J. (2014). Mining of massive datasets. Chapter 9.
• [21] Leskovec, J., Rajaraman, A., Ullman J. (2014). Mining of massive datasets. Chapter 11.
• [22] Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097-1105).
• [23] Ciresan, D., Meier, U., Schmidhuber, J. (2012, June). Multi-column deep neural networks for image classification. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on (pp. 3642-3649). IEEE.
• [24] Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine, IEEE, 29(6), 82-97.