Towards a Scalable Dynamic Spatial Database System

With the rise of GPS-enabled smartphones and other similar mobile devices, massive amounts of location data are available. However, no scalable solutions for soft real-time spatial queries on large sets of moving objects have yet emerged. In this paper we explore and measure the limits of actual algorithms and implementations regarding different application scenarios. And finally we propose a novel distributed architecture to solve the scalability issues.Comment: (2012

Distributed Spatial Data Sharing: a new era in sharing spatial data

The advancements in information and communications technology, including the widespread adoption of GPS-based sensors, improvements in computational data processing, and satellite imagery, have resulted in new data sources, stakeholders, and methods of producing, using, and sharing spatial data. Daily, vast amounts of data are produced by individuals interacting with digital content and through automated and semi-automated sensors deployed across the environment. A growing portion of this information contains geographic information directly or indirectly embedded within it. The widespread use of automated smart sensors and an increased variety of georeferenced media resulted in new individual data collectors. This raises a new set of social concerns around individual geopricacy and data ownership. These changes require new approaches to managing, sharing, and processing geographic data. With the appearance of distributed data-sharing technologies, some of these challenges may be addressed. This can be achieved by moving from centralized control and ownership of the data to a more distributed system. In such a system, the individuals are responsible for gathering and controlling access and storing data. Stepping into the new area of distributed spatial data sharing needs preparations, including developing tools and algorithms to work with spatial data in this new environment efficiently. Peer-to-peer (P2P) networks have become very popular for storing and sharing information in a decentralized approach. However, these networks lack the methods to process spatio-temporal queries. During the first chapter of this research, we propose a new spatio-temporal multi-level tree structure, Distributed Spatio-Temporal Tree (DSTree), which aims to address this problem. DSTree is capable of performing a range of spatio-temporal queries. We also propose a framework that uses blockchain to share a DSTree on the distributed network, and each user can replicate, query, or update it. Next, we proposed a dynamic k-anonymity algorithm to address geoprivacy concerns in distributed platforms. Individual dynamic control of geoprivacy is one of the primary purposes of the proposed framework introduced in this research. Sharing data within and between organizations can be enhanced by greater trust and transparency offered by distributed or decentralized technologies. Rather than depending on a central authority to manage geographic data, a decentralized framework would provide a fine-grained and transparent sharing capability. Users can also control the precision of shared spatial data with others. They are not limited to third-party algorithms to decide their privacy level and are also not limited to the binary levels of location sharing. As mentioned earlier, individuals and communities can benefit from distributed spatial data sharing. During the last chapter of this work, we develop an image-sharing platform, aka harvester safety application, for the Kakisa indigenous community in northern Canada. During this project, we investigate the potential of using a Distributed Spatial Data sharing (DSDS) infrastructure for small-scale data-sharing needs in indigenous communities. We explored the potential use case and challenges and proposed a DSDS architecture to allow users in small communities to share and query their data using DSDS. Looking at the current availability of distributed tools, the sustainable development of such applications needs accessible technology. We need easy-to-use tools to use distributed technologies on community-scale SDS. In conclusion, distributed technology is in its early stages and requires easy-to-use tools/methods and algorithms to handle, share and query geographic information. Once developed, it will be possible to contrast DSDS against other data systems and thereby evaluate the practical benefit of such systems. A distributed data-sharing platform needs a standard framework to share data between different entities. Just like the first decades of the appearance of the web, these tools need regulations and standards. Such can benefit individuals and small communities in the current chaotic spatial data-sharing environment controlled by the central bodies

Learning structure and schemas from heterogeneous domains in networked systems: a survey

The rapidly growing amount of available digital documents of various formats and the possibility to access these through internet-based technologies in distributed environments, have led to the necessity to develop solid methods to properly organize and structure documents in large digital libraries and repositories. Specifically, the extremely large size of document collections make it impossible to manually organize such documents. Additionally, most of the document sexist in an unstructured form and do not follow any schemas. Therefore, research efforts in this direction are being dedicated to automatically infer structure and schemas. This is essential in order to better organize huge collections as well as to effectively and efficiently retrieve documents in heterogeneous domains in networked system. This paper presents a survey of the state-of-the-art methods for inferring structure from documents and schemas in networked environments. The survey is organized around the most important application domains, namely, bio-informatics, sensor networks, social networks, P2Psystems, automation and control, transportation and privacy preserving for which we analyze the recent developments on dealing with unstructured data in such domains.Peer ReviewedPostprint (published version

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Application Platforms, Routing Algorithms and Mobility Behavior in Mobile Disruption-Tolerant Networks

Mobile disruption-tolerant networks (DTNs), experience frequent and long duration partitions due to the low density of mobile nodes. In these networks, traditional networking models relying on end-to-end communication cease to work. The topological characteristics of mobile DTNs impose unique challenges for the design and validation of routing protocols and applications. We investigate challenges of mobile DTNs from three different viewpoints: the application layer, a routing perspective, and by studying mobility patterns. In the application layer, we have built 7DS (7th Degree of Separation) as a modular platform to develop mobile disruption-tolerant applications. 7DS offers a class of disruption-tolerant applications to exchange data with other mobile users in the mobile DTN or with the global Internet. In the routing layer, we have designed and implemented PEEP as an interest-aware and energy efficient routing protocol which automatically extracts individual interests of mobile users and estimates the global popularity of data items throughout the network. PEEP considers mobile users' interests and global popularity of data items in its routing decisions to route data toward the community of mobile users who are interested in that data content. Mobility of mobile users impacts the conditions in which routing protocols for mobile DTNs must operate and types of applications that could be provided for mobile networks in general. The current synthetic mobility models do not reflect real-world mobile users' behavior. Trace-based mobility models, also, are based on traces that either represent a specific population of mobile users or do not have enough granularities in representing mobility of mobile users for example cell tower traces. We use Sense Networks' GPS traces that are being collected by monitoring a broad spectrum of mobile users. Using these traces, we employ a Markovian approach to extract inherent patterns in human mobility. We design and implement a new routing algorithm for mobile DTNs based on our Markovian analysis of the human mobility. We explore how the knowledge of the mobility improves the performance of our Markov based routing algorithm. We show that that our Markov based routing algorithm increases the rate of data delivery to popular destinations with consuming less energy than legacy algorithms

Partitioning and Offloading for IoT and Video Streaming Applications that Utilize Computing Resources at the Network Edge

The Internet of Things (IoT) is a concept in which physical objects embedded with sensors, actuators, and network connectivity can communicate and react to their surroundings. IoT applications connect physical objects for the purpose of decision making by sensing and analysing generated data from the embedded sensors in physical objects. IoT applications are growing rapidly as sensors become less expensive. Sensors generate large amounts of data that may meaningless unless the data is used to derive knowledge with in a certain period of time. Stream processing paradigm is used by IoT to provide requirements of IoT applications. In a stream processing paradigm, unlike traditional data bases, data is not stored but rather processed as it is generated. To transfer generated data from distributed data sources to a processing center such as cloud may not allow for real-time processing due to the network delay. Another high-demand application is live streaming of video. The performance of live video stream systems is inferior when there is a sudden large demand in the number of users. This thesis addresses some of the limitations of current architectures for video streaming systems and IoT applications based on the use of nearby computing resources (e.g., cloudlet, fog). First, we addressed the degrading performance in video stream systems when a flash crowd occurs. The performance of video streaming systems is affected by flash crowd and degrade the quality of service for subscribers to the content delivery system. A flash crowd happens when there is a sudden large increase in the number of users. Therefore, flash crowds increase network traffic for any particular server. The main challenge is to make sure that the video streaming system has sufficient capacity to handle the occurrence of flash crowds. Second, we address the limitation of current architectures for running mobile applications by introducing a dynamic partitioning and offloading of a mobile application. Mobile devices have limited resources including short battery life, storage capacity and processor performance. This limits the applications that can run on it. Mobile applications can be partitioned so that some of the application runs on a cloud. This works well for applications with relatively little data to be transferred and that do not have a high level of interaction with the user. Challenges with applications that have large amounts of data to be transferred and have a high level interactiveness is the high latency incurred by the network and packet loss of the wireless network. A mobile application can be partitioned so that part of it runs on a nearby computing resource e.g., fog node or cloudlet. This thesis presents a framework that introduces fine-grained offloading approach and support for runtime and dynamic partitioning of an application. Third, we present a solution for placement of stream operators over distributed fog nodes for live processing of data streams from geographically distributed data sources. This placement of stream operators takes place in such a way that it supports applications with a high volume of data that require real-time (or near real-time) analysis To this end, this thesis proposed a set of algorithms for placement of stream operators among fog nodes

A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data

Abstract The Management of mobile networks has become so complex due to a huge number of devices, technologies and services involved. Network optimization and incidents management in mobile networks determine the level of the quality of service provided by the communication service providers (CSPs). Generally, the down time of a system and the time taken to repair [mean time to repair (MTTR)] has a direct impact on the revenue, especially on the operational expenditure (OPEX). A fast root cause analysis (RCA) mechanism is therefore crucial to improve the efficiency of the operational team within the CSPs. This paper proposes a quadri-dimensional approach (i.e. services, subscribers, handsets and cells) to build a service quality management (SQM) tree in a Big Data platform. This is meant to speed up the root cause analysis and prioritize the elements impacting the performance of the network. Two algorithms have been proposed; the first one, to normalize the performance indicators and the second one to build the SQM tree by aggregating the performance indicators for different dimensions to allow ranking and detection of tree paths with the worst performance. Additionally, the proposed approach will allow CSPs to detect the mobile network dimensions causing network issues in a faster way and protect their revenue while improving the quality of the service delivered