Data Mining - Concepts and Techniques.

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.

/pdf/data-mining-concepts-and-techniques-2nd-edition-zx1xx1f5e3.pdf

Data Mining: Concepts and Techniques (2nd edition)

Matrix Factorization Techniques for Recommender Systems

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006

Query recommendation, which suggests related queries to search engine users, has attracted a lot of attention in recent years. Most of the existing solutions, which perform analysis of users’ search history (or query logs), are often insufficient for long-tail queries that rarely appear in query logs. To handle such queries, we study the use of entities found in queries to provide recommendations. Specifically, we extract entities from a query, and use these entities to explore new ones by consulting an information source. The discovered entities are then used to suggest new queries to the user. In this article, we examine two information sources: (1) a knowledge base (or KB), such as YAGO and Freebase; and (2) a click log, which contains the URLs accessed by a query user. We study how to use these sources to find new entities useful for query recommendation. We further study a hybrid framework that integrates different query recommendation methods effectively. As shown in the experiments, our proposed approaches provide better recommendations than existing solutions for long-tail queries. In addition, our query recommendation process takes less than 100ms to complete. Thus, our solution is suitable for providing online query recommendation services for search engines.

Entity-Based Query Recommendation for Long-Tail Queries

The results of traditional spatial queries (ie, range search, nearest neighbor, etc) are usually meaningless in spatio-temporal applications, because they will be invalidated by the movements of query and/or data objects In practice, a query result R should be accompanied with validity information specifying (i) the (future) time T that R will expire, and (ii) the change C of R at time T (so that R can be updated incrementally) Although several algorithms have been proposed for this problem, their worst-case performance is the same as that of sequential scan This paper presents the first theoretical study on validity queries, and develops indexes and algorithms with attractive I/O complexities Our discussion covers numerous important variations of the problem and different query/object mobility combinations The solutions involve a set of non-trivial reductions that reveal the problem characteristics and permit the deployment of existing structures

/pdf/validity-information-retrieval-for-spatio-temporal-queries-2l710kmptf.pdf

Validity information retrieval for spatio-temporal queries: Theoretical performance bounds

Recently, composite-order bilinear pairing has been shown to be useful in many cryptographic constructions. However, it is time-costly to evaluate. This is because the composite order should be at least 1024bit and, hence, the elliptic curve group order n and base field become too large, rendering the bilinear pairing algorithm itself too slow to be practical (e.g., the Miller loop is Ω(n)). Thus, composite-order computation easily becomes the bottleneck of a cryptographic construction, especially, in the case where many pairings need to be evaluated at the same time. The existing solution to this problem that converts composite-order pairings to prime-order ones is only valid for certain constructions. In this paper, we leverage the huge number of threads available on Graphics Processing Units (GPUs) to speed up composite-order pairing computation. We investigate suitable SIMD algorithms for base/extension field, elliptic curve and bilinear pairing computation as well as mapping these algorithms into GPUs with careful considerations. Experimental results show that our method achieves a record of 8.7ms per pairing on a 80bit security level, which is a 20-fold speedup compared to the state-of-the-art CPU implementation. This result also opens the road to adopting higher security levels and using rich-resource parallel platforms, which for example are available in cloud computing. For example, we can achieve a record of 7 ×10−6 USD per pairing on the Amazon cloud computing environment.

/pdf/acceleration-of-composite-order-bilinear-pairing-on-graphics-458ag487la.pdf

Acceleration of composite order bilinear pairing on graphics hardware

Recently, composite-order bilinear pairing has been shown to be useful in many cryptographic constructions. However, it is time-costly to evaluate. This is because the composite order should be at least 1024bit and, hence, the elliptic curve group order n and base field become too large, rendering the bilinear pairing algorithm itself too slow to be practical (e.g., the Miller loop is Ω(n)). Thus, composite-order computation easily becomes the bottleneck of a cryptographic construction, especially, in the case where many pairings need to be evaluated at the same time. The existing solution to this problem that converts composite-order pairings to prime-order ones is only valid for certain constructions. In this paper, we leverage the huge number of threads available on Graphics Processing Units (GPUs) to speed up composite-order pairing computation. We investigate suitable SIMD algorithms for base field, extension field, elliptic curve and bilinear pairing computation as well as mapping these algorithms into GPUs with careful considerations. Experimental results show that our method achieves a record of 8.7ms per pairing on a 1024bit security level, which is a 20-fold speedup compared to state-of-the-art CPU implementation. This result also opens the road to adopting higher security levels and using rich-resource parallel platforms, which for example are available in cloud computing. In fact, we can achieve more than 24 times speedup on a 2048bit security level and a record of 7× 10−6 USD per pairing on the Amazon cloud computing environment.

/pdf/acceleration-of-composite-order-bilinear-pairing-on-graphics-4pipkprllx.pdf

Nikos Mamoulis

Papers

Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006

Entity-Based Query Recommendation for Long-Tail Queries

Validity information retrieval for spatio-temporal queries: Theoretical performance bounds

Acceleration of composite order bilinear pairing on graphics hardware

Acceleration of Composite Order Bilinear Pairing on Graphics Hardware.