George Mavrommatis

Bio: George Mavrommatis is an academic researcher from University of Thessaly. The author has contributed to research in topics: Spatial query & Closest pair of points problem. The author has an hindex of 5, co-authored 20 publications receiving 109 citations. Previous affiliations of George Mavrommatis include University of Piraeus & Aristotle University of Thessaloniki.

Efficient processing of all-k-nearest-neighbor queries in the MapReduce programming framework

Abstract: Numerous modern applications, from social networking to astronomy, need efficient answering of queries on spatial data. One such query is the All k Nearest-Neighbor Query, or k Nearest-Neighbor Join, that takes as input two datasets and, for each object of the first one, returns the k nearest-neighbors from the second one. It is a combination of the k nearest-neighbor and join queries and is computationally demanding. Especially, when the datasets involved fall in the category of Big Data, a single machine cannot efficiently process it. Only in the last few years, papers proposing solutions for distributed computing environments have appeared in the literature. In this paper, we focus on parallel and distributed algorithms using the Apache Hadoop framework. More specifically, we focus on an algorithm that was recently presented in the literature and propose improvements to tackle three major challenges that distributed processing faces: improvement of load balancing (we implement an adaptive partitioning scheme based on Quadtrees), acceleration of local processing (we prune points during calculations by utilizing plane-sweep processing), and reduction of network traffic (we restructure and reduce the output size of the most demanding phase of computation). Moreover, by using real 2D and 3D datasets, we experimentally study the effect of each improvement and their combinations on performance of this literature algorithm. Experiments show that by carefully addressing the three aforementioned issues, one can achieve significantly better performance. Thereby, we conclude to a new scalable algorithm that adapts to the data distribution and significantly outperforms its predecessor. Moreover, we present an experimental comparison of our algorithm against other well-known MapReduce algorithms for the same query and show that these algorithms are also significantly outperformed.

A comparison of distributed spatial data management systems for processing distance join queries

Abstract: Due to the ubiquitous use of spatial data applications and the large amounts of spatial data that these applications generate, the processing of large-scale distance joins in distributed systems is becoming increasingly popular. Two of the most studied distance join queries are the K Closest Pair Query (KCPQ) and the \(\varepsilon \) Distance Join Query (\(\varepsilon \) DJQ). The KCPQ finds the K closest pairs of points from two datasets and the \(\varepsilon \) DJQ finds all the possible pairs of points from two datasets, that are within a distance threshold \(\varepsilon \) of each other. Distributed cluster-based computing systems can be classified in Hadoop-based and Spark-based systems. Based on this classification, in this paper, we compare two of the most current and leading distributed spatial data management systems, namely SpatialHadoop and LocationSpark, by evaluating the performance of existing and newly proposed parallel and distributed distance join query algorithms in different situations with big real-world datasets. As a general conclusion, while SpatialHadoop is more mature and robust system, LocationSpark is the winner with respect to the total execution time.

Technical and tactical analysis of goal scoring patterns in the 2018 fifa world cup in russia

Abstract: The aims of this research are to record and analyze the goals scored during the 64 matches in the 21st World Cup in Russia in 2018, to highlight those factors that are directly related to the teams’ effectiveness in scoring, to record the goals approved after the use of the new technologies, video assistant referee and goal line technology, as well as their effect on the outcome of the game. Chi-square and univariate general linear methods were used for the data analysis. Statistical difference is observed in the number of goals scored between the two halves (X²=8.699, p<0.005). A comparable percentage of scoring in most of the periods with exceptions of the periods in over time, the period 16th to 30th min, the addition time of the first and second half and the period 76th to 90th min of the game is determined (p<0.01). The teams that scored first won 71.4% matches, lost 9.5% and had a tie 19% of the matches (X²=42.000, p<0.001). Most of the goals were scored following a corner kick (24), penalty (22) and free kick scored non-directly (16), which were significantly different from free kick scored directly (6) and throw in (2) (X²=26.857, p<0.001). 19 goals were scored from cross, 19 from a long-range shot (8 of them with the “inner foot”), 18 from a forward pass and 12 from cutback. Statistical differences between the first four groups and all of the others are determined (X²=27.818, p<0.01). Significant differences were found between the goals that “began” from the offensive third, the middle and the defensive third (X²=73.645, p<0.001). 58.9% of the goals are scored following positional play, which is significantly different compared to counter attack (29.5%) and direct play (11.6%) (X²=32.611, p<0.001). Over 59 goals were scored from “the inner part of the foot” or “place”, and 31 from header, which were significantly different from the other types of shot (X²=89.254, p<0.001).

将“Cooperative Learning”融入课堂——浅谈英语素质教育

Abstract: 1. Interaction. The interactions learners have with each other build interpersonal skills, such as listening, politely interrupting, expressing ideas, raising questions, disagreeing, paraphrasing, negotiating, and asking for help. 2. Interdependence. Learners must depend on one another to accomplish a common objective. Each group member has specific tasks to complete, and successful completion of each member’s tasks results in attaining the overall group objective.

Information retrieval

Abstract: The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

An exact algorithm for the maximum clique problem with accelerated pruning

Abstract: We present an exact partial enumerative algorithm for the maximum clique problem. The pruning device used is derived from graph colorings. Pruning of the search tree is accomplished not only by the number of colors used to color a tree subproblem but also by using information gained in the process of coloring. This leads to increased pruning which translates into improved computational performance. Experimental results on test problems are presented.

Adaptive and Intelligent Systems for Collaborative Learning Support: A Review of the Field

Abstract: This study critically reviews the recently published scientific literature on the design and impact of adaptive and intelligent systems for collaborative learning support (AICLS) systems. The focus is threefold: 1) analyze critical design issues of AICLS systems and organize them under a unifying classification scheme, 2) present research evidence on the impact of these systems on student learning, and 3) identify current trends and open research questions in the field. After systematically searching online bibliographic databases, 105 articles were included in the review with 70 of them reporting concrete evaluation data on the learning impact of AICLS systems. Systems design analysis led us to propose a classification scheme with five dimensions: pedagogical objective, target of adaptation, modeling, technology, and design space. The reviewed articles indicate that AICLS systems increasingly introduce Artificial Intelligence and Web 2.0 techniques to support pretask interventions, in-task peer interactions, and learning domain-specific activities. Findings also suggest that AICLS systems may improve both learners' domain knowledge and collaboration skills. However, these benefits are subject to the learning design and the capability of AICLS to adapt and intervene in an unobtrusive way. Finally, providing peer interaction support seems to motivate students and improve collaboration and learning.