Sohaib Ghani

Taghreed: a system for querying, analyzing, and visualizing geotagged microblogs

This paper presents Taghreed; a full-fledged system for efficient and scalable querying, analyzing, and visualizing geotagged microblogs, e.g., tweets. Taghreed supports arbitrary queries on a large number (Billions) of microblogs that go up to several months in the past. Taghreed consists of four main components: (f) Indexer, (2) query engine, (3) recovery manager, and (4) visualizer. Taghreed indexer efficiently digests incoming microblogs with high arrival rates in light memory-resident indexes. When the memory becomes full, a flushing policy manager transfers the memory contents to disk indexes which are managing Billions of microblogs for several months. On memory failure, the recovery manager restores the system status from replicated copies for the main-memory content. Taghreed query engine consists of two modules: a query optimizer and a query processor. The query optimizer generates an optimal query plan to be executed by the query processor through efficient retrieval techniques to provide low query response, i.e., order of milli-seconds. Taghreed visualizer allows end users to issue a wide variety of spatio-temporal queries. Then, it graphically presents the answers and allows interactive exploration through them. Taghreed is the first system that addresses all these challenges collectively for microblogs data. In the paper, each system component is described in detail.

SHAHED: A MapReduce-based system for querying and visualizing spatio-temporal satellite data

Remote sensing data collected by satellites are now made publicly available by several space agencies. This data is very useful for scientists pursuing research in several applications including climate change, desertification, and land use change. The benefit of this data comes from its richness as it provides an archived history for over 15 years of satellite observations for natural phenomena such as temperature and vegetation. Unfortunately, the use of such data is very limited due to the huge size of archives (> 500TB) and the limited capabilities of traditional applications. This paper introduces SHAHED; a MapReduce-based system for querying, visualizing, and mining large scale satellite data. SHAHED considers both the spatial and temporal aspects of the data to provide efficient query processing at large scale. The core of SHAHED is composed of four main components. The uncertainty component recovers missing data in the input which comes from cloud coverage and satellite mis-alignment. The indexing component provides a novel multi-resolution quad-tree-based spatio-temporal index structure, which indexes satellite data efficiently with minimal space overhead. The querying component answers selection and aggregate queries in real-time using the constructed index. Finally, the visualization component uses MapReduce programs to generate heat map images and videos for user queries. A set of experiments running on a live system deployed on a cluster of machines show the efficiency of the proposed design. All the features supported by SHAHED are made accessible through an easy to use Web interface that hides the complexity of the system and provides a nice user experience.

Deception detection in Twitter

Online Social Networks (OSNs) play a significant role in the daily life of hundreds of millions of people. However, many user profiles in OSNs contain deceptive information. Existing studies have shown that lying in OSNs is quite widespread, often for protecting a user’s privacy. In this paper, we propose a novel approach for detecting deceptive profiles in OSNs. We specifically define a set of analysis methods for detecting deceptive information about user genders and locations in Twitter. First, we collected a large dataset of Twitter profiles and tweets. Next, we defined methods for gender guessing from Twitter profile colors and names. Subsequently, we apply Bayesian classification and K-means clustering algorithms to Twitter profile characteristics (e.g., profile layout colors, first names, user names, and spatiotemporal information) and geolocations to analyze the user behavior. We establish the overall accuracy of each indicator through extensive experimentation with our crawled dataset. Based on the outcomes of our approach, we are able to detect deceptive profiles about gender and location with a reasonable accuracy.

ThemeDelta: Dynamic Segmentations over Temporal Topic Models

We present ThemeDelta, a visual analytics system for extracting and visualizing temporal trends, clustering, and reorganization in time-indexed textual datasets. ThemeDelta is supported by a dynamic temporal segmentation algorithm that integrates with topic modeling algorithms to identify change points where significant shifts in topics occur. This algorithm detects not only the clustering and associations of keywords in a time period, but also their convergence into topics (groups of keywords) that may later diverge into new groups. The visual representation of ThemeDelta uses sinuous, variable-width lines to show this evolution on a timeline, utilizing color for categories, and line width for keyword strength. We demonstrate how interaction with ThemeDelta helps capture the rise and fall of topics by analyzing archives of historical newspapers, of U.S. presidential campaign speeches, and of social messages collected through iNeighbors, a web-based social website. ThemeDelta is evaluated using a qualitative expert user study involving three researchers from rhetoric and history using the historical newspapers corpus.

Demonstration of Taghreed: A system for querying, analyzing, and visualizing geotagged microblogs

This paper demonstrates Taghreed; a full-fledged system for efficient and scalable querying, analyzing, and visualizing geotagged microblogs, such as tweets. Taghreed supports a wide variety of queries on all microblogs attributes. In addition, it is able to manage a large number (billions) of microblogs for relatively long periods, e.g., months. Taghreed consists of four main components: (1) indexer, (2) query engine, (3) recovery manager, and (4) visualizer. Taghreed indexer efficiently digests incoming microblogs with high arrival rates in light main-memory indexes. When the memory becomes full, the memory contents are flushed to disk indexes which are managing billions of microblogs efficiently. On memory failure, the recovery manager restores the memory contents from backup copies. Taghreed query engine consists of two modules: a query optimizer and a query processor. The query optimizer generates an optimized query plan to be executed by the query processor to provide low query responses. Taghreed visualizer features to its users a wide variety of spatiotemporal queries and presents the answers on a map-based user interface that allows an interactive exploration. Taghreed is the first system that addresses all these challenges collectively for geotagged microblogs data. The system is demonstrated based on real system implementation through different scenarios that show system functionality and internals.

VacationFinder: a tool for collecting, analyzing, and visualizing geotagged Twitter data to find top vacation spots

Choosing a location for vacations and weekends usually confuses many people. This concern has attracted considerable attention in recent years as currently there is no application based on actual visitors that helps people in finding out the top places for vacations. Online social networks such as Twitter are becoming very popular in last few years and can help in this regard. People nowadays generally do check-ins at new places. Also, analysis of tweets tagged with geolocation and time can provide trends of top vacation spots. In this paper, we present VacationFinder; a novel location-based application that uses geotagged tweets to help people in where they should spend their holidays and weekends. We use real Twitter data crawled since October 2013. We apply indexing, spatio-temporal querying, and machine learning techniques to check, analyze, and filter the user activities in a particular country before and after a specific holiday. We then visualize the results and give our recommendations of top vacation spots for a particular holiday. The paper includes use cases on top vacation spots for Saudis in spring break of 2014 both inside as well as outside Saudi Arabia. Our application can not only help people but can also give direction to governmental agencies about promoting tourism in the country. It can also help law enforcement agencies, advertisement industry, and various businesses such as restaurants and shopping stores about where to focus during a particular holiday.

VisCAT: spatio-temporal visualization and aggregation of categorical attributes in twitter data

In the last few years, Twitter data has become so popular that it is used in a rich set of new applications, e.g., real-time event detection, demographic analysis, and news extraction. As user-generated data, the plethora of Twitter data motivates several analysis tasks that make use of activeness of 271+ Million Twitter users. This demonstration presents VisCAT; a tool for aggregating and visualizing categorical attributes in Twitter data. VisCAT outputs visual reports that provide spatial analysis through interactive map-based visualization for categorical attributes---such as tweet language or source operating system---at different zoom levels. The visual reports are built based on user-selected data in arbitrary spatial and temporal ranges. For this data, VisCAT employs a hierarchical spatial data structure to materialize the count of each category at multiple spatial levels. We demonstrate VisCAT, using real Twitter dataset. The demonstration includes use cases on tweet language and tweet source attributes in the region of Gulf Arab states, which can be used for deducing thoughtful conclusions on demographics and living levels in local societies.

A demonstration of Shahed: A MapReduce-based system for querying and visualizing satellite data

Several space agencies such as NASA are continuously collecting datasets of earth dynamics-e.g., temperature, vegetation, and cloud coverage-through satellites. This data is stored in a publicly available archive for scientists and researchers and is very useful for studying climate, desertification, and land use change. The benefit of this data comes from its richness as it provides an archived history for over 15 years of satellite observations. Unfortunately, the use of such data is very limited due to the huge size of archives (> 500TB) and the limited capabilities of traditional applications. In this demo, we present Shahed, an interactive system which provides an efficient way to index, query, and visualize satellite datasets available in NASA archive. Shahed is composed of four main modules. The uncertainty module resolves data uncertainty imposed by the satellites. The indexing module organizes the data in a novel multi-resolution spatio-temporal index designed for satellite data. The querying module uses the indexes to answer both spatiotemporal selection and aggregate queries provided by the user. The visualization module generates images, videos, and multi-level images which gives an insight of data distribution and dynamics over time. This demo gives users a hands-on experience with Shahed through a map-based web interface in which users can browse the available datasets using the map, issue spatiotemporal queries, and visualize the results as images or videos.

A therapy-driven gamification framework for hand rehabilitation

Rehabilitative therapy is usually very expensive and confined to specialized rehabilitation centers or hospitals, leading to slower recovery times for corresponding patients. Therefore, there is a high demand for the development of technology-based personalized solutions to guide and encourage patients towards performing online rehabilitation program that can help them live independently at home. This paper introduces an innovative e-health framework that develops adaptive serious games for people with hand disabilities. The aim of this work is to provide a patient-adaptive environment for the gamification of hand therapies in order to facilitate and encourage rehabilitation issues. Theoretical foundations (i.e., therapy and patient models) and algorithms to match therapy-based hand gestures to navigational movements in 3D space within the serious game environment have been developed. A novel game generation module is introduced, which translates those movements into a 3D therapy-driven route on a real-world map and with different levels of difficulty based on the patient profile and capabilities. In order to enrich the user navigation experience, a 3D spatio-temporal validation region is also generated, which tracks and adjusts the patient movements throughout the session. The gaming environment also creates and adds semantics to different types of attractive and repellent objects in space depending on the difficulty level of the game. Relevant benchmarks to assess the patient interaction with the environment along with a usability and performance testing of our framework are introduced to ensure quantitative as well as qualitative improvements. Trial tests in one disability center were conducted with a total number of five subjects, having hand motor controls problems, who used our gamified physiotherapy solution to help us in measuring the usability and users’ satisfaction levels. The obtained results and feedback from therapists and patients are very encouraging.

MultiNode-Explorer: A Visual Analytics Framework for Generating Web-based Multimodal Graph Visualizations

We propose MultiNode-Explorer, a visual analytics framework that is capable of transforming multidimensional datasets into an entity-relationship (E-R) model and visualizing the data as node-link diagrams. The framework accepts an E-R schema, a set of relational data tables, and an interface specification file, and generates a multimodal and multivariate graph and a corresponding interactive applet for viewing the graph in a web browser. We show examples for a large research organization, a visual search engine for a Wiki, and for NSF funding data.