"The widespread use of mobile devices and the real time availability of user-location information is facilitating the development of new personalized, location-based applications and services (LBSs). Such applications require multi-attribute query processing, handling of high access scalability, support for millions of users, real time querying capability and analysis of large volumes of data. Cloud computing aided a new generation of distributed databases commonly known as key-value stores. Key-value stores were designed to extract value from very large volumes of data while being highly available, fault-tolerant and scalable, hence providing much needed features to support LBSs. However complex queries on multidimensional data cannot be processed efficiently as they do not provide means to access multiple attributes. In this thesis we present MGrid, a unifying indexing framework which enables key-value stores to support multidimensional queries. We organize a set of nodes in a P-Grid overlay network which provides fault-tolerance and efficient query processing. We use Hilbert Space Filling Curve based linearization technique which preserves the data locality to efficiently manage multi-dimensional data in a key-value store. We propose algorithms to dynamically process range and k nearest neighbor (kNN) queries on linearized values. This removes the overhead of maintaining a separate index table. Our approach is completely independent from the underlying storage layer and can be implemented on any cloud infrastructure. Experiments on Amazon EC2 show that MGrid achieves a performance improvement of three orders of magnitude in comparison to MapReduce and four times to that of MDHBase scheme"--Abstract, page iii.

This handbook provides a glimpse of the research that is underway in smart cities, with an examination of the relevant issues. It describes software infrastructures for smart cities, the role of 5G and Internet of things in future smart cities scenarios, the use of clouds and sensor-based devices for monitoring and managing smart city facilities, a variety of issues in the emerging field of urban informatics, and various smart city applications. Handbook of Smart Cities includes fifteen chapters from renowned worldwide researchers working on various aspects of smart city scale cyber-physical systems. It is intended for researchers, developers of smart city technologies and advanced-level students in the fields of communication systems, computer science, and data science. This handbook is also designed for anyone wishing to find out more about the on-going research thrusts and deployment experiences in smart cities. It is meant to provide a snapshot of the state-of-the-art at the time of its writing in several software services and cyber infrastructures as pertinent to smart cities. This handbook presents application case studies in video surveillance, smart parking, and smart building management in the smart city context. Unique experiences in designing and implementing the applications or the issues involved in developing smart city level applications are described in these chapters. Integration of machine learning into several smart city application scenarios is also examined in some chapters of this handbook.

Spatial Big Data is considered an essential trend in future scientific and business applications. Indeed, research instruments, medical devices, and social networks generate hundreds of peta bytes of spatial data per year. However, as many authors have pointed out, the lack of specialized frameworks dealing with such kind of data is limiting possible applications and probably precluding many scientific breakthroughs. In this thesis, we describe three HPC scientific applications, ranging from molecular dynamics, neuroscience analysis, and physics simulations, where we experience first hand the limits of the existing technologies. Thanks to our experience, we define the desirable missing functionalities, and we focus on two features that when combined significantly improve the way scientific data is analyzed. On one side, scientific simulations generate complex datasets where multiple correlated characteristics describe each item. For instance, a particle might have a space position (x,y,z) at a given time (t). If we want to find all elements within the same area and period, we either have to scan the whole dataset, or we must organize the data so that all items in the same space and time are stored together. The second approach is called Multidimensional Indexing (MI), and it uses different techniques to cluster and to organize similar data together. On the other side, approximate analytics has been often indicated as a smart and flexible way to explore large datasets in a short period. Approximate analytics includes a broad family of algorithms which aims to speed up analytical workloads by relaxing the precision of the results within a specific interval of confidence. For instance, if we want to know the average age in a group with 1-year precision, we can consider just a random fraction of all the people, thus reducing the amount of calculation. But if we also want less I/O operations, we need efficient data sampling, which means organizing data in a way that we do not need to scan the whole data set to generate a random sample of it. According to our analysis, combining Multidimensional Indexing with efficient data Sampling (MIS) is a vital missing feature not available in the current distributed data management solutions. This thesis aims to solve such a shortcoming and it provides novel scalable solutions. At first, we describe the existing data management alternatives; then we motivate our preference for NoSQL key-value databases. Secondly, we propose an analytical model to study the influence of data models on the scalability and performance of this kind of distributed database. Thirdly, we use the analytical model to design two novel multidimensional indexes with efficient data sampling: the D8tree and the AOTree. Our first solution, the D8tree, improves state of the art for approximate spatial queries on static and mostly read dataset. Later, we enhanced the data ingestion capability or our approach by introducing the AOTree, an algorithm that enables the query performance of the D8tree even for HPC write-intensive applications. We compared our solution with PostgreSQL and plain storage, and we demonstrate that our proposal has better performance and scalability. Finally, we describe Qbeast, the novel distributed system that implements the D8tree and the AOTree using NoSQL technologies, and we illustrate how Qbeast simplifies the workflow of scientists in various HPC applications providing a scalable and integrated solution for data analysis and management.

This book presents innovative ideas, cutting-edge findings, and novel techniques, methods, and applications in a broad range of cybersecurity and cyberthreat intelligence areas. As our society becomes smarter, there is a corresponding need to secure our cyberfuture. The book describes approaches and findings that are of interest to business professionals and governments seeking to secure our data and underpin infrastructures, as well as to individual users.

Advances in Geo-Spatial Information Science presents recent advances regarding fundamental issues of geo-spatial information science (space and time, spatial analysis, uncertainty modeling and geo-visualization), and new scientific and technological research initiatives for geo-spatial information science (such as spatial data mining, mobile data modeling, and location-based services). The book contains selected and revised papers presented at the joint International Conference on Theory, Data Handling and Modelling in GeoSpatial Information Science (Hong Kong, 26–28 May 2010), and brings together three related international academic communities: spatial information science, spatial data handling, and modeling geographic systems. Advances in Geo-Spatial Information Science will be of interest for academics and professionals interested in spatial information science, spatial data handling, and modeling of geographic systems.

​This book covers the theory, design and applications of computer networks, distributed computing and information systems. Networks of today are going through a rapid evolution, and there are many emerging areas of information networking and their applications. Heterogeneous networking supported by recent technological advances in low-power wireless communications along with silicon integration of various functionalities such as sensing, communications, intelligence and actuations is emerging as a critically important disruptive computer class based on a new platform, networking structure and interface that enable novel, low-cost and high-volume applications. Several of such applications have been difficult to realize because of many interconnections problems. To fulfill their large range of applications, different kinds of networks need to collaborate, and wired and next-generation wireless systems should be integrated in order to develop high-performance computing solutions to problems arising from the complexities of these networks. The aim of the book “Advanced Information Networking and Applications” is to provide latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to the emerging areas of information networking and applications.

In this monograph, we study the problem of high-dimensional indexing and systematically introduce two efficient index structures: one for range queries and the other for similarity queries. Extensive experiments and comparison studies are conducted to demonstrate the superiority of the proposed indexing methods. Many new database applications, such as multimedia databases or stock price information systems, transform important features or properties of data objects into high-dimensional points. Searching for objects based on these features is thus a search of points in this feature space. To support efficient retrieval in such high-dimensional databases, indexes are required to prune the search space. Indexes for low-dimensional databases are well studied, whereas most of these application specific indexes are not scaleable with the number of dimensions, and they are not designed to support similarity searches and high-dimensional joins.