The coronavirus outbreak is first and foremost a human calamity, severely affecting the health of millions of people around the globe. It is also having a significant impact on the national and global economy. As this crisis has a two-sided negative impact, health, and economy, this makes it hard to manage. A full lockdown of the society can help control the spread of the infection. However, the economy will suffer significantly. On the other side, allowing normal life activities will protect the growth of the economy. But, it will decisively increase the spread of the infection, and consequently, cause a collapse in the whole medical system. In response to the COVID-19 pandemic, this paper presents the development of a real-time crisis management system that is able to holistically control the country’s resources at both micro and macro levels. The ultimate goal of this system is to assure the harmonic balance between crucial actions needed for the containment of the coronavirus spread and at the same time protect the national economy from the negative impacts caused by these actions. To achieve this goal, the system will privately monitor users’ geo-social interactions to assist in applying a reasonable social distance when it is needed. The system will alert the people who were in interaction with an infected person or area and recommend the appropriate health care provider. In addition to the fine crafted mobile and web interfaces, the proposed system will be equipped with a set of infrastructures such as data warehousing, data mining, maps, and dashboards. These tools will facilitate heterogeneous data management and analytics to efficiently handle the COVID-19 pandemic crisis at various levels of the decision-making process. We present the proposed crisis management system which can significantly contribute to winning the battle with COVID-19.

Publicly available data is increasing rapidly, and will continue to grow with the advancement of technologies in sensors, smartphones and the Internet of Things. Data from multiple sources can improve coverage and provide more relevant knowledge about surrounding events and points of Interest. The strength of one source of data can compensate for the shortcomings of another source by providing supplementary information. Maps are also getting popular day-by-day and people are using it to achieve their daily task smoothly and efficiently. Starting from paper maps hundred years ago, multiple type of maps are available with point of interest, real-time traffic update or displaying micro-blogs from social media. In this paper, we introduce Hadath, a system that displays multi-resolution live events of interest from a variety of available data sources. The system has been designed to be able to handle multiple type of inputs by encapsulating incoming unstructured data into generic data packets. System extracts local events of interest from generic data packets and identify their spatio-temporal scope to display such events on a map, so that as a user changes the zoom level, only events of appropriate scope are displayed. This allows us to show live events in correspondence to the scale of view - when viewing at a city scale, we see events of higher significance, while zooming in to a neighbourhood, events of a more local interest are highlighted. The final output creates a unique and dynamic map browsing experience. Finally, to validate our proposed system, we conducted experiments on social media data.

The widespread use of GPS-enabled smartphones along with the popularity of micro-blogging and social networking applications, e.g., Twitter and Facebook, has resulted in the generation of huge streams of geo-tagged textual data. Many applications require real-time processing of these streams. For example, location-based ad-targeting systems enable advertisers to register millions of ads to millions of users based on the users' location and textual profile. Existing streaming systems are either centralized or are not spatial-keyword aware, and hence these systems cannot efficiently support the processing of rapidly arriving spatial-keyword data streams. In this paper, we introduce a two-layered indexing scheme for the distributed processing of spatial-keyword data streams. We realize this indexing scheme in Tornado, a distributed spatial-keyword streaming system. The first layer, termed the routing layer, is used to fairly distribute the workload, and furthermore, co-locate the data objects and the corresponding queries at the same processing units. The routing layer uses the Augmented-Grid, a novel structure that is equipped with an efficient search algorithm for distributing the data objects and queries. The second layer, termed the evaluation layer, resides within each processing unit to reduce the processing overhead. The two-layered index adapts to changes in the workload by applying a cost formula that continuously represents the processing overhead at each processing unit. Extensive experimental evaluation using real Twitter data indicates that Tornado achieves high scalability and more than 2x improvement over the baseline approach in terms of the overall system throughput.

International audience

Advances in location-based services (LBS) demand high-throughput processing of both static and streaming data. Recently, many systems have been introduced to support distributed main-memory processing to maximize the query throughput. However, these systems are not optimized for spatial data processing. In this demonstration, we showcase Cruncher, a distributed main-memory spatial data warehouse and streaming system. Cruncher extends Spark with adaptive query processing techniques for spatial data. Cruncher uses dynamic batch processing to distribute the queries and the data streams over commodity hardware according to an adaptive partitioning scheme. The batching technique also groups and orders the overlapping spatial queries to enable inter-query optimization. Both the data streams and the offline data share the same partitioning strategy that allows for data co-locality optimization. Furthermore, Cruncher uses an adaptive caching strategy to maintain the frequently-used location data in main memory. Cruncher maintains operational statistics to optimize query processing, data partitioning, and caching at runtime. We demonstrate two LBS applications over Cruncher using real datasets from OpenStreetMap and two synthetic data streams. We demonstrate that Cruncher achieves order(s) of magnitude throughput improvement over Spark when processing spatial data.

Recommending an optimized path for a large crowd poses a unique challenge to existing routing algorithms due to the interactions between users and the dynamic changes over road networks. Industries, researchers and end users show an enormous interest in crowdsourced data comprising social networks and user-generated content to remain updated with their concerns. In this paper, we present a data collection framework that helps users to find optimized routes in a dynamic environment. We have developed a data collection framework to collect dynamic road conditions via a set of location-based services to support a very large Hajj crowd by capturing their locations using smartphones. We also collect geotagged social network data that provides more details about road conditions. The system leverages geotagged crowdsourced information to identify constraints such as accidents, congestions, and roadblocks. Moreover, by continuously collecting real-time geotagged data of moving users, the system can also find the flow of traffic and road conditions. We propose a spatial grid index to compute the optimized path, and to identify the affected users within impact zones. The plan is to test the whole application and back-end server during Hajj 2016, where over three million pilgrims from all over the world gather to perform their rituals.

Crowdsourced multimedia data poses several challenges when it is collected, stored, indexed, retrieved, and visualized. Examples of crowd source multimedia data are social sensors, vehicle sensors, physical sensors, human sensors, etc. Analyzing such multimodal and diversified crowdsourced data provides very rich understanding about the need of individuals within a crowd. Such understanding makes it possible to tailor services to individuals' needs, also called context-aware services. In this paper, we propose a spatial multimedia big data framework that can collect multimedia data from 1) a very large crowd equipped with multi-sensory smartphones, 2) vehicles, and 3) social networks. A set of multimedia services are offered to users to support their spatio-temporal activities. These include but not limited to 1) simple user interfaces to utilize multimedia services for instant guidance, 2) navigation to points of interests (POI), and 3) efficient and cost effective intra-city rides to users. The big data framework is designed to handle a very large number of multimedia spatio-temporal queries in real-time. The system is a pilot project and will be deployed during the event of Hajj 2015 when over three million pilgrims from all over the world will visit Makkah, Saudi Arabia to perform their Hajj rituals.

Social networks, such as Facebook, Instagram, and Twitter, provide intuitive ways to share a variety of information including geotagged multimedia data within users' communities of interest (COI) or publicly in real-time. Real-time geotagged multimedia data can provide semantics to conventional spatial queries in order to enrich the user navigation experience. In this paper, we introduce a mechanism to process spatio-temporal queries by leveraging geotagged multimedia data such as images, audio, video, and text, in order to add semantics to the conventional queries. Our framework collects, stores, and spatially tags multimedia data shared by users through social networks or through our developed mobile application. The system then uses such data in order to enhance the conventional routing services by resolving existing usability issues and by providing semantics to the routes in terms of enriched points of interest while taking dynamic road conditions into account. A proof of concept of the system will be demonstrated with the following spatio-temporal queries on road networks: 1) multimedia-enhanced shortest path queries; 2) multimedia-enhanced k-nearest neighbor queries; and 3) multimedia-enhanced range queries. Finally, a novel technique for finding lost individuals using geotagged multimedia data is also introduced. The results are tailor-made to the users' smartphone bandwidth and resolution requirements

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.

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.

The proliferation of networked home appliances, coupled with the popularity of low cost gesture detection sensors has made it possible to create smart home environments where users can manipulate devices of daily use through gestures. In this paper, we present a multi-sensory environment that allows a disabled person to control actual devices around the house that are presented to her in a mixed reality environment, as part of occupational therapy exercises. An analytical engine processes the motion data collected by gesture recognition sensors, measures recovery metrics, such as joint range of motion and speed of movement, etc. and presents the results to the therapist or the patient in the form of graphs and exercise statistics. We have incorporated intuitive 2D and 3D user interfaces into the environment to make the therapy experience more engaging and immersive for the disabled people. From the multimedia occupational therapy exercise data analysis, a therapist can get determine the ability of a patient to perform daily life activities without the help of others.

Traditional routing algorithms for calculating the fastest or shortest path become ineffective or difficult to use when both source and destination are dynamic or unknown. To solve the problem, we propose a novel semantic routing system that leverages geo-tagged rich crowdsourced multimedia information such as images, audio, video and text to add semantics to the conventional routing. Our proposed system includes a Semantic Multimedia Routing Algorithm (SMRA) that uses an indexed spatial big data environment to answer multimedia spatio-temporal queries in real-time. The results are customized to the users' smartphone bandwidth and resolution requirements. The system has been designed to be able to handle a very large number of multimedia spatio-temporal requests at any given moment. A proof of concept of the system will be demonstrated through two scenarios. These are 1) multimedia enhanced routing and 2) finding lost individuals in a large crowd using multimedia. We plan to test the system's performance and usability during Hajj 2015, where over four million pilgrims from all over the world gather to perform their rituals.

Logging on to a system using a conventional keyboard may not be feasible in certain environments, such as, in a surgical operation theatre or in an industrial manufacturing facility. We have developed a multi-sensory gesture based login system that allows a user to access secure information using body gestures. The system can be configured to use different types of gestures according to the type of sensors available to the user. We have proposed a simple scheme to represent all alphanumeric characters required for password entry as gestures within the multi-sensory environment. Our scheme is scalable enough to support sensors that detect a large number of gestures to those that can only accept a few. This allows the system to be used in a variety of situations such as usage by disabled persons with limited ability to perform gestures. We are in the midst of deploying our developed system in a clinical environment.

Traditional routing algorithms for calculating the fastest or shortest path become ineffective or difficult to use when both source and destination are dynamic or unknown. To solve the problem, we propose a novel semantic routing system that leverages geo-tagged rich crowdsourced multimedia information such as images, audio, video and text to add semantics to the conventional routing. Our proposed system includes a Semantic Multimedia Routing Algorithm (SMRA) that uses an indexed spatial big data environment to answer multimedia spatio-temporal queries in real-time. The results are customized to the users' smartphone bandwidth and resolution requirements. The system has been designed to be able to handle a very large number of multimedia spatio-temporal requests at any given moment. A proof of concept of the system will be demonstrated through two scenarios. These are 1) multimedia enhanced routing and 2) finding lost individuals in a large crowd using multimedia. We plan to test the system's performance and usability during Hajj 2015, where over four million pilgrims from all over the world gather to perform their rituals.

The widespread use of location-aware devices together with the increased popularity of micro-blogging applications (e.g., Twitter) led to the creation of large streams of spatio-textual data. In order to serve real-time applications, the processing of these large-scale spatio-textual streams needs to be distributed. However, existing distributed stream processing systems (e.g., Spark and Storm) are not optimized for spatial/textual content. In this demonstration, we introduce Tornado, a distributed in-memory spatio-textual stream processing server that extends Storm. To efficiently process spatio-textual streams, Tornado introduces a spatio-textual indexing layer to the architecture of Storm. The indexing layer is adaptive, i.e., dynamically re-distributes the processing across the system according to changes in the data distribution and/or query workload. In addition to keywords, higher-level textual concepts are identified and are semantically matched against spatio-textual queries. Tornado provides data deduplication and fusion to eliminate redundant textual data. We demonstrate a prototype of Tornado running against real Twitter streams, where the users can register continuous or snapshot spatio-textual queries using a map-assisted query-interface.

The proliferation of multimedia big data for dissemination and sharing of massive amounts of information raises important security and privacy concerns. One such concern is the composition and enforcement of privacy policies in order to securely manage access of multimedia big data. Several researchers have pointed out that for proper enforcement of privacy policies, the privacy requirements should be captured in access control systems. In this paper, we propose a hybrid approach where privacy requirements are captured in an access control system and present a framework for composition and enforcement of privacy policies. The focus is to allow a user, not a system or security administrator to compose conflict free policies for their online multimedia data. An additional requirement is that such a policy be context-aware. We also present a methodology for verifying the privacy policy in order to ensure correctness and logical consistency. The verification process is also used to ensure that sensitive security requirements are not violated when privacy rules are enforced. A prototype, named Intelligent Privacy Manager (iPM), has been implemented for sharing of multimedia big data in a secure and private manner.

Designing a comprehensive curriculum for information system engineering (ISE) that accounts for the needs and expectations of the students, the current trends and advances in the field, and the skills required by the job market is an extremely demanding task. The curriculum must balance the highest academic standards and integrity against the emerging expectations of the market. Here, we identify the specific requirements of a newly commissioned ISE curriculum, and we explain the framework we adopted and developed in the process. We based the framework on a commonly recognized information system model namely service-oriented architecture (SOA). Thus, we derived the different knowledge areas to be covered in the curriculum from the layers of the SOA style.

The plethora of lacation-aware devices has led to countless location-based services in which huge amounts of spatio-temporal data get created everyday. Several applications requie efficient processing of queries on the locations of moving objects over time, i.e., the moving object trajectories. This calls for efficient trajectory-based indexing methods that capture both the spatial and temporal dimensions of the data in a way that minimizes the number of disk I/Os required for both updating and querying. Motivated by applications that require only the recent history of a moving object's trajectory, this paper introduces the trails-tree; a disk-based data structure for indexing recent trajectories. The trails-tree maintains a temporal-sliding window over the trajectories and uses: (1) an in-memory memo structure that reduces the I/O cost of updates using a lazy-update mechanism, and (2) a lazy vacuum-cleaning mechanism to delete parts of the trajectories that fall out of the sliding window. Experimental evaluation illustrates that the trails-tree outperforms the state-of-the-art index structures for indexing recent trajectory data by up to a factor of two.

In this paper, we present a novel idea of a map-based therapy environment for people with Hemiplegia. The therapy environment is designed according to the suggestions of therapists, which consists of a spatial map browsing serious game augmented with our novel multi-sensory natural user interface (NUI). The NUI is based on 3D motion sensors that can recognize different hand and body gestures used for browsing a 3D or 2D map. The 3D motion sensors work in a non-invasive way; hence, they do not require any wearable body attachments and can be used at home without assistance from the therapists. The map-browsing environment provides an immersive experience to the disabled users, which helps in performing therapy in an interesting and entertaining manner. We have developed analytics for measuring certain quality of health improvement metrics from each type of spatial map browsing movements. The 3D motion sensors have been tested with Nokia, Google, ESRI, and a number of other maps that allow a subject to visualize and browse the 3D and 2D maps of the world. The map browsing session data shows the nature of big data; hence, the session data is stored in a cloud environment. Our developed serious game environment is web-based; thus anyone having the appropriate low cost sensor hardware can plug it in and start experiencing a natural way of hands free map browsing. We have deployed our framework in a hospital that treats Hemiplegic patients. Based on the feedback obtained, the developed platform shows a huge potential for use in hospitals that provide physiotherapy services as well as at patients' home as an assistive therapeutic service.

Recently, the use of non-invasive ways to track joint motion in the human body has drawn significant attention in the therapy domain. One of the reasons for this popularity is due to availability of economically priced 3D motion sensors. In this paper, we present a web-based 3D interactive serious game interface that uses non-invasive methods to recognize the movements of the body. Motion data of a subject is collected through two motion sensors, a Kinect and a LEAP, in a non-invasive manner. Joint motion along with inverse kinematic joint information is displayed in 3D environment in a live manner or recorded for offline replaying and data analysis. To facilitate the complex therapy authoring process, the system incorporates an authoring tool that allows a therapist to design a complex therapy in terms of primitive therapies and assign it to a patient. The subject as well as other members of the community of interest such as therapists, parents and caregivers can view the results at any time and can follow up with patient's progress.

This paper presents a multimedia e-health framework to conduct therapy sessions by collecting live therapeutic data of patients in a non-invasive way. Using our proposed framework, a therapist can model complex gestures by mapping them to a set of primitive actions and generate high-level therapies. Two inexpensive 3D motion tracking sensors, a Kinect and a Leap, are used to collect motion data of a given subject. Data can then be displayed on screen in a live manner or recorded for offline replaying and analysis. The system incorporates an intelligent authoring tool for therapy design, and produces live plots that show the quality of improvement metrics for a given patient.

A Knowledge Cube, or cube for short, is an intelligent and adaptive database instance capable of storing, analyzing, and searching data. Each cube is established based on semantic aspects, e.g., (1) Topical, (2) Contextual, (3) Spatial, or (4) Temporal. A cube specializes in handling data that is only relevant to the cube's semantics. Knowledge cubes are inspired by two prime architectures: (1) Dataspaces that provides an abstraction for data management where heterogeneous data sources can co-exist and it requires no prespecified unifying schema, and (2) Linked Data that provides best practices for publishing and interlinking structured data on the web. A knowledge cube uses Linked Data as its main building block for its data layer and encompasses some of the data integration abstractions defined by Dataspaces. In this paper, knowledge cubes are proposed as a semantically-guided data management architecture, where data management is influenced by the data semantics rather than by a predefined scheme. Knowledge cubes support the five pillars of Big Data also known as the five V's, namely Volume, Velocity, Veracity, Variety, and Value. Interesting opportunities can be leveraged when learning the semantics of the data. This paper highlights these opportunities and proposes a strawman design for knowledge cubes along with the research challenges that arise when realizing them.

In this paper we propose a multimedia environment that can capture kinematic data from live gestures of a child having Hemiplegia disability and generate live analytical results to be useful for decision making system of a therapist. The kinematic data is obtained from some clinically suggested therapy modules that are used to monitor quality of improvement of a disabled child, which includes exercises involving the affected joints and muscles. The proposed environment uses the 3D depth sensing Microsoft Kinect device to detect, recognize and track the movement of different key joints of the body and deduce kinematic data from these movements. The method is non-invasive as the child does not need to wear any external devices in the body. The proposed environment incorporates Second Life serious game environment where the live therapeutic movement of child, therapist and one's community of interest is synchronized between physical and virtual world. Finally, we share our preliminary test data, which is validated by the therapists from three different disability hospitals that treat children with Hemiplegia.

In this paper, we present an interactive multimedia environment that can be used to effectively complement the role of a therapist in the process of rehabilitation for disabled children. We use Microsoft Kinect 3D depth sensing camera with the online Second Life virtual world to record rehabilitation exercises performed by a physiotherapist or a disabled child. The exercise session can be played synchronously in Second Life. The physical activities of the users are synchronized with their virtual counterparts in the Second Life. The exercises can be recorded as well and made available for downloading to facilitate offline playback. A disabled child can follow the exercise at home in the absence of the therapist, since the system can provide visual guidance for performing the exercise in the right manner. Using the proposed system, parents at home can also assist the disabled child in performing therapy sessions in the absence of a therapist. Following the suggestions of therapists, the developed prototype can track several gestures of children who have mobility problems. Using a single Kinect device, we can capture high resolution joint movement of the body, without the need for any complicated hardware set up. The initial joint-based angular measurements show promising potential of our prototype to be deployed in real physiotherapy sessions.