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.

Computation and Monitoring of Exclusive Closest Pairs

Abstract: Given two datasets A and B, their exclusive closest pairs (ECP) join is a one-to-one assignment of objects from the two datasets, such that (i) the closest pair (a,b) in A times B is in the result and (ii) the remaining pairs are determined by removing objects a,b from A,B respectively, and recursively searching for the next closest pair. A real application of exclusive closest pairs is the computation of (car, parking slot) assignments. This paper introduces the problem and proposes several solutions that solve it in main-memory, exploiting space partitioning. In addition, we define a dynamic version of the problem, where the objective is to continuously monitor the ECP join solution, in an environment where the joined datasets change positions and content. Finally, we study an extended form of the query, where objects in one of the two joined sets (e.g., parking slots) have a capacity constraint, allowing them to match with multiple objects from the other set (e.g., cars). We show how our techniques can be extended for this variant and compare them with a previous solution to this problem. Experimental results on a system prototype demonstrate the efficiency and applicability of the proposed algorithms.

Geo-Social Co-location Mining

Abstract: Modern technology to capture geo-spatial information produces a huge flood of geo-spatial and geo-spatio-temporal data with a new user mentality of utilizing this technology to voluntarily share information. This location information, enriched with social information, is a new source to discover new and useful knowledge. This work introduces geo-social co-location mining, the problem of finding social groups that are frequently found at the same location. This problem has applications in social sciences, allowing to research interactions between social groups and permitting social-link prediction. It can be divided into two sub-problems. The first sub-problem of finding spatial co-location instances, requires to properly address the inherent uncertainty in geo-social network data, which is a consequence of generally very sparse check-in data, and thus very sparse trajectory information. For this purpose, we propose a probabilistic model to estimate the probability of a user to be located at a given location at a given time, creating the notion of probabilistic co-locations. The second sub-problem of mining the resulting probabilistic co-location instances requires efficient methods for large databases having a high degree of uncertainty. Our approach solves this problem by extending solutions for probabilistic frequent itemset mining. Our experimental evaluation performed on real (but anonymized) geo-social network data shows the high efficiency of our approach, and its ability to find new social interactions.

Parallel In-Memory Evaluation of Spatial Joins

Abstract: We study the in-memory and parallel evaluation of spatial joins, by tuning a classic partitioning based algorithm. Our study shows that, compared to a straightforward implementation of the algorithm, performance can be improved significantly. We also show how to select appropriate partitioning parameters based on data statistics, in order to tune the algorithm for the given join inputs. Our parallel implementation scales gracefully with the number of threads reducing the cost of the join to at most one second even for join inputs with tens of millions of rectangles.

Retrieval of Spatial Join Pattern Instances from Sensor Networks

Abstract: We study the continuous evaluation of spatial join queries and extensions thereof, defined by interesting combinations of sensor readings (events) that co-occur in a spatial neighborhood. An example of such a pattern is "a high temperature reading in the vicinity of at least four high-pressure readings". We devise acquisitional and distributed protocols for evaluating this class of queries, aiming at the minimization of energy consumption. Cases of simple and complex join queries with single or multi-hop distance constraints are considered. Finally, we experimentally compare the effectiveness of the proposed solutions on an experimental platform that simulates real sensor networks. Our results show that acquisitional protocols perform best for multi-hop or high-selectivity queries while distributed techniques should be applied for the remaining cases.

Joint search by social and spatial proximity

Abstract: The diffusion of social networks introduces new challenges and opportunities for advanced services, especially so with their ongoing addition of location-based features. We show how applications like company and friend recommendation could significantly benefit from incorporating social and spatial proximity, and study a query type that captures these two-fold semantics. We develop highly scalable algorithms for its processing, and use real social network data to empirically verify their efficiency and efficacy.

“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!.