Nikos Mamoulis

Bio: Nikos Mamoulis is an academic researcher from University of Ioannina. The author has contributed to research in topics: Joins & Spatial query. The author has an hindex of 56, co-authored 282 publications receiving 11121 citations. Previous affiliations of Nikos Mamoulis include University of Hong Kong & Max Planck Society.

Improving microblog retrieval from exterior corpus by automatically constructing a microblogging corpus

Abstract: A large-scale training corpus consisting of microblogs belonging to a desired category is important for high-accuracy microblog retrieval. Obtaining such a large-scale microblgging corpus manually is very time and labor-consuming. Therefore, some models for the automatic retrieval of microblogs from an exterior corpus have been proposed. However, these approaches may fail in considering microblog-specific features. To alleviate this issue, we propose a methodology that constructs a simulated microblog-ging corpus rather than directly building a model from the exterior corpus. The performance of our model is better since the microblog-special knowledge of the microblogging corpus is used in the end by the retrieval model. Experimental results on real-world microblogs demonstrate the superiority of our technique compared to the previous approaches.

Flow Computation in Temporal Interaction Networks

Abstract: Temporal interaction networks capture the history of activities between entities along a timeline. At each interaction, some quantity of data (money, information, kbytes, etc.) flows from one vertex of the network to another. Flow-based analysis can reveal important information. For instance, financial intelligent units (FIUs) are interested in finding subgraphs in transactions networks with significant flow of money transfers. In this paper, we introduce the flow computation problem in an interaction network or a subgraph thereof. We propose and study two models of flow computation, one based on a greedy flow transfer assumption and one that finds the maximum possible flow. We show that the greedy flow computation problem can be easily solved by a single scan of the interactions in time order. For the harder maximum flow problem, we propose graph precomputation and simplification approaches that can greatly reduce its complexity in practice. As an application of flow computation, we formulate and solve the problem of flow pattern search, where, given a graph pattern, the objective is to find its instances and their flows in a large interaction network. We evaluate our algorithms using real datasets. The results show that the techniques proposed in this paper can greatly reduce the cost of flow computation and pattern enumeration.

Maximizing a record's standing in a relation

Abstract: Given a database table with records that can be ranked, an interesting problem is to identify selection conditions, which are qualified by an input record and render its ranking as high as possible among the qualifying tuples. In this paper, we study this standing maximization problem, which finds application in object promotion and characterization. We propose greedy methods, which are experimentally shown to achieve high accuracy compared to exhaustive enumeration, while scaling very well to the problem size. Our contributions include a lineartime algorithm for determining the optimal selection range for an attribute and techniques for choosing and prioritizing the most promising selection predicates to apply. Experiments on real datasets confirm the effectiveness and efficiency of our techniques.

Three-dimensional Geospatial Interlinking with JedAI-spatial

Abstract: Geospatial data constitutes a considerable part of (Semantic) Web data, but so far, its sources are inadequately interlinked in the Linked Open Data cloud. Geospatial Interlinking aims to cover this gap by associating geometries with topological relations like those of the Dimensionally Extended 9-Intersection Model. Due to its quadratic time complexity, various algorithms aim to carry out Geospatial Interlinking efficiently. We present JedAI-spatial , a novel, open-source system that organizes these algorithms according to three dimensions: (i) Space Tiling , which determines the approach that reduces the search space, (ii) Budget-awareness , which distinguishes interlinking algorithms into batch and progressive ones, and (iii) Execution mode , which discerns between serial algorithms, running on a single CPU-core, and parallel ones, running on top of Apache Spark. We analytically describe JedAI-spatial’s architecture and capabilities and perform thorough experiments to provide in-teresting insights about the relative performance of its algorithms.

Privacy Preservation by Disassociation TR-IMIS-2011-1

Abstract: In this work, we focus on the preservation of user privacy in the publication of sparse multidimensional data. Existing works protect the users’ sensitive information by generalizing or suppressing quasi identifiers in the original data. In many real world cases, neither generalization nor the distinction between sensitive and non-sensitive items is appropriate. For example, web search query logs contain millions of terms that are very hard to categorize as sensitive or non sensitive. At the same time, a generalization-based anonymization would remove the most valuable information in the dataset; the original terms. Motivated by this problem, we propose an anonymization technique termed disassociation that preserves the original terms but hides the fact that two or more different terms appear in the same record. Up to now, such techniques were used to sever the link between quasiidentifiers and sensitive values in settings with a clear distinction between these types of values. Our proposal generalizes these techniques for sparse multidimensional data, where no such distinction holds. We protect the users’ privacy by disassociating combinations of terms that can act as quasi-identifiers from the rest of the record or by disassociating the constituent terms, so that the identifying combination cannot be accurately recognized. To this end, we present an algorithm that anonymizes the data by first clustering them and then locally disassociating identifying combinations of terms. We analyze the attack model and extend the km-anonymity guaranty to the aforementioned setting. We empirically evaluate our method on real and synthetic datasets.

“Bioinformatics” 특집을 내면서

Abstract: 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.

Data Mining: Concepts and Techniques (2nd edition)

Abstract: 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.

When is nearest neighbor meaningful

Abstract: 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!.