Murat Ali Bayir

Identifying breakpoints in public opinion

While polls are traditionally used for observing public opinion, they provide a point snapshot, not a continuum. We consider the problem of identifying breakpoints in public opinion, and propose using micro-blogging sites to capture trends in public opinion. We develop methods to detect changes in public opinion, and find events that cause these changes. Our experiments show that the proposed methods are able to determine changes in public opinion and extract the major news about the events effectively. We also deploy an application where users can view the important news stories for a continuing event and find the related articles on web.

Genetic Algorithm for the Multiple-Query Optimization Problem

Producing answers to a set of queries with common tasks efficiently is known as the multiple-query optimization (MQO) problem. Each query can have several alternative evaluation plans, each with a different set of tasks. Therefore, the goal of MQO is to choose the right set of plans for queries which minimizes the total execution time by performing common tasks only once. Since MQO is an NP-hard problem, several, mostly heuristics based, solutions have been proposed for solving it. To the best of our knowledge, this correspondence is the first attempt to solve MQO using an evolutionary technique, genetic algorithms

iMAP: Indirect measurement of air pollution with cellphones

In this paper, we introduce the cellphone-based indirect sensing problem. While participatory sensing aims at monitoring of a phenomenon by deploying a dense set of sensors carried by individuals, our indirect sensing problem aims at inferring the manifestations of a sparsely monitored phenomenon on the individuals. The main advantage of the indirect sensing method is that, by making use of existing exposure modeling and estimation methods, it provides a more feasible alternative to direct sensing. Collection of time-location logs using the cellphones plays a major role in our indirect sensing method, while direct sensing at the cellphones is unneeded. We focus on the air pollutant exposure estimation problem as an application of the indirect sensing technique and propose a web-based framework, iMAP, for addressing this problem. We also discuss the information quality (IQ) requirements of indirect sensing in the iMAP framework.

Discovering better navigation sequences for the session construction problem

In this paper, we propose a novel page view based session model and session construction method to address the Web Usage Mining (WUM) problem. Unlike the simple session models, where sessions are sequences of web pages requested from the server (or served from a browser/proxy cache) and viewed in the browser (which may not guarantee a direct relationship between subsequent web pages in the session), we define a more realistic session model in which a session is a set of paths traversed in the web graph that corresponds to a user navigation performed by following links on web pages. We define the session construction process from raw server logs as a new graph problem and present a novel algorithm, Smart-SRA (Smart Session Reconstruction Algorithm), to solve this problem efficiently. An experimental evaluation based on data collected from real web access scenarios showed that Smart-SRA produces more accurate user sessions than the session construction methods found in the literature.

Improving the performance of Hadoop Hive by sharing scan and computation tasks

MapReduce is a popular programming model for executing time-consuming analytical queries as a batch of tasks on large scale data clusters. In environments where multiple queries with similar selection predicates, common tables, and join tasks arrive simultaneously, many opportunities can arise for sharing scan and/or join computation tasks. Executing common tasks only once can remarkably reduce the total execution time of a batch of queries. In this study, we propose a Multiple Query Optimization framework, SharedHive, to improve the overall performance of Hadoop Hive, an open source SQL-based data warehouse using MapReduce. SharedHive transforms a set of correlated HiveQL queries into a new set of insert queries that will produce all of the required outputs within a shorter execution time. It is experimentally shown that SharedHive achieves significant reductions in total execution times of TPC-H queries.

Performance Comparison of Pattern Discovery Methods on Web Log Data

One of the popular trends in computer science has been development of intelligent web-based systems. Demand for such systems forces designers to make use of knowledge discovery techniques on web server logs. Web usage mining has become a major area of knowledge discovery on World Wide Web. Frequent pattern discovery is one of the main issues in web usage mining. These frequent patterns constitute the basic information source for intelligent web-based systems. In this paper; frequent pattern mining algorithms for web log data and their performance comparisons are examined. Our study is mainly focused on finding suitable pattern mining algorithms for web server logs.

Track me! a web based location tracking and analysis system for smart phone users

Mobility information of cell phone users is very important for wide range of applications, including context-based search and advertising, early warning systems, city-wide sensing applications such as air pollution exposure estimation and traffic planning. With the inclusion of new technologies in the cell phone hardware such as built-in GPS and 802.11 supports, mobility information are easily captured, managed and forwarded to a remote system via opportunistic connections over Internet. However, it is very difficult to use these low level location data for practical applications due to lack of sufficient information including high level location and temporal data. In order to solve this problem, we propose a Web Based Mobility Analysis System which collects location data from cell phone users via opportunistic Internet connections and convert these low level location data to high level mobility information as well as adding a temporal dimension. In our experiments, we have illustrated the benefits of our systems on the Reality Mining data set which contains 350K hours of real cell tower connection data.

Robust heuristic algorithms for exploiting the common tasks of relational cloud database queries

Graphical abstractDisplay Omitted HighlightsMQO is adapted for relational Cloud DB with a cost model including network expenses.Alternative query plans are intelligently developed and experimentally evaluated.B&B, Genetic, Hill Climbing and Genetic-Hill Climbing algorithms are developed. Cloud computing enables a conventional relational database systems hardware to be adjusted dynamically according to query workload, performance and deadline constraints. One can rent a large amount of resources for a short duration in order to run complex queries efficiently on large-scale data with virtual machine clusters. Complex queries usually contain common subexpressions, either in a single query or among multiple queries that are submitted as a batch. The common subexpressions scan the same relations, compute the same tasks (join, sort, etc.), and/or ship the same data among virtual computers. The total time spent for the queries can be reduced by executing these common tasks only once. In this study, we build and use efficient sets of query execution plans to reduce the total execution time. This is an NP-Hard problem therefore, a set of robust heuristic algorithms, Branch-and-Bound, Genetic, Hill Climbing, and Hybrid Genetic-Hill Climbing, are proposed to find (near-) optimal query execution plans and maximize the benefits. The optimization time of each algorithm for identifying the query execution plans and the quality of these plans are analyzed by extensive experiments.

Trend sensing via Twitter

Due to its ever increasing popularity, Twitter has become a pervasive information outlet. In this paper, we present a passive sensing framework for identifying trends via Twitter. In our framework, we use a multi-dimensional corpus for fine-granularity sensing of trends, and employ both vector-space and set-space methods for achieving accuracy. We present two applications of our framework. The first one is sensing trends in public opinion by using an emotion-category corpus. The second application is sensing trends in location-types in a city by using a location-category corpus. Our experiments show that the proposed methods are able to determine changes in trends effectively in both application scenarios.

A Web-Based Personalized Mobility Service for Smartphone Applications

Nowadays, most of the basic web services use instant location information for providing suitable content to smartphone users. However, more intelligent smartphone applications such as context-based search and advertising, early warning systems and city-wide sensing applications may require additional information about smartphone users such as their mobility profiles. To meet more personalized demand of these applications we propose TRACK ME: A new web-based framework for smartphone applications with personalized lightweight mobility service as well as location tracking and mobility profile construction. We showed that our personalized mobility service supports different smartphone applications and it is lightweight enough to provide fast access to the mobility profiles of smartphone users. We illustrate the benefits of our mobility service on two smartphone applications: location prediction and air pollution exposure risk estimation.