Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

Building standardized and secure mobile health services based on social media

Mobile devices and social media have been used to create empowering healthcare services. However, privacy and security concerns remain. Furthermore, the integration of interoperability biomedical standards is a strategic feature. Thus, the objective of this paper is to build enhanced healthcare services by merging all these components. Methodologically, the current mobile health telemonitoring architectures and their limitations are described, leading to the identification of new potentialities for a novel architecture. As a result, a standardized, secure/private, social-media-based mobile health architecture has been proposed and discussed. Additionally, a technical proof-of-concept (two Android applications) has been developed by selecting a social media (Twitter), a security envelope (open Pretty Good Privacy (openPGP)), a standard (Health Level 7 (HL7)) and an information-embedding algorithm (modifying the transparency channel, with two versions). The tests performed included a small-scale and a boundary scenario. For the former, two sizes of images were tested; for the latter, the two versions of the embedding algorithm were tested. The results show that the system is fast enough (less than 1 s) for most mHealth telemonitoring services. The architecture provides users with friendly (images shared via social media), straightforward (fast and inexpensive), secure/private and interoperable mHealth services

Using RGB colour combination in coloured quick response (QR) code algorithm to enhance QR code capacity

A Quick Response (QR) Code is a two-dimensional barcode that stores characters and can be read by any smartphone camera. The QR code has the capability to encode various data formats and languages; nevertheless, existing black and white QR code offers limited data storage. Even though there exist research on coloured QR Code to increase the storage capacity, requirement for larger data capacity by end user keep increasing. Hence, this thesis proposes a coloured QR Code algorithm which utilizes RGB colour combination to allow a larger data storage. The proposed algorithm integrates the use of compression, multiplexing, and multilayer techniques in encoding and decoding the QR code. Furthermore, it also introduces a partial encoding/decoding algorithm that allows the stored data to be manipulated. The algorithm that includes encoding and decoding processes is based on the red, green, and blue (RGB) colour techniques, which are used to create high capacity coloured QR code. This is realised in the experiments that store American Standard Code for Information Interchange (ASCII) characters. The ASCII text characters are used as an input and performance is measured by the number of characters that can be stored in a single black and white QR code version 40 (i.e. the benchmark) and also the coloured QR code. Other experiment metrics include percentage of missing characters, number of produced QR code, and elapsed time to create the QR code. Simulation results indicate that the proposed algorithm stores 29 times more characters than the black and white QR code and 9 times more than other coloured QR code. Hence, this shows that the coloured QR Code has the potential of becoming a useful mini-data storage as it does not rely on internet connection

A System for Privacy-Preserving Mobile Health and Fitness Data Sharing: Design, Implementation and Evaluation

The growing spread of smartphones and other mobile devices has given rise to a number of health and fitness applications. Users can track their calorie intake, get reminders to take their medication, and track their fitness workouts. Many of these services have social components, allowing users to find like-minded peers, compete with their friends, or participate in open challenges. However, the prevalent service model forces users to disclose all of their data to the service provider. This may include sensitive information, like their current position or medical conditions. In this thesis, we will design, implement and evaluate a privacy-preserving fitness data sharing system. The system provides privacy not only towards other users, but also against the service provider, does not require any Trusted Third Parties (TTPs), and is backed by strong cryptography. Additionally, it hides the communication metadata (i.e. who is sharing data with whom). We evaluate the security of the system with empirical and formal methods, including formal proofs for parts of the system. We also investigate the performance with empirical data and a simulation of a large-scale deployment. Our results show that the system can provide strong privacy guarantees. However, it incurs a significant networking overhead for large deployments

Optimization of scientific algorithms in heterogeneous systems and accelerators for high performance computing

Actualmente, la computación de propósito general en GPU es uno de los pilares básicos de la computación de alto rendimiento. Aunque existen cientos de aplicaciones aceleradas en GPU, aún hay algoritmos científicos poco estudiados. Por ello, la motivación de esta tesis ha sido investigar la posibilidad de acelerar significativamente en GPU un conjunto de algoritmos pertenecientes a este grupo. En primer lugar, se ha obtenido una implementación optimizada del algoritmo de compresión de vídeo e imagen CAVLC (Context-Adaptive Variable Length Encoding), que es el método entrópico más usado en el estándar de codificación de vídeo H.264. La aceleración respecto a la mejor implementación anterior está entre 2.5x y 5.4x. Esta solución puede aprovecharse como el componente entrópico de codificadores H.264 software, y utilizarse en sistemas de compresión de vídeo e imagen en formatos distintos a H.264, como imágenes médicas. En segundo lugar, se ha desarrollado GUD-Canny, un detector de bordes de Canny no supervisado y distribuido. El sistema resuelve las principales limitaciones de las implementaciones del algoritmo de Canny, que son el cuello de botella causado por el proceso de histéresis y el uso de umbrales de histéresis fijos. Dada una imagen, esta se divide en un conjunto de sub-imágenes, y, para cada una de ellas, se calcula de forma no supervisada un par de umbrales de histéresis utilizando el método de MedinaCarnicer. El detector satisface el requisito de tiempo real, al ser 0.35 ms el tiempo promedio en detectar los bordes de una imagen 512x512. En tercer lugar, se ha realizado una implementación optimizada del método de compresión de datos VLE (Variable-Length Encoding), que es 2.6x más rápida en promedio que la mejor implementación anterior. Además, esta solución incluye un nuevo método scan inter-bloque, que se puede usar para acelerar la propia operación scan y otros algoritmos, como el de compactación. En el caso de la operación scan, se logra una aceleración de 1.62x si se usa el método propuesto en lugar del utilizado en la mejor implementación anterior de VLE. Esta tesis doctoral concluye con un capítulo sobre futuros trabajos de investigación que se pueden plantear a partir de sus contribuciones

Selected Papers from the First International Symposium on Future ICT (Future-ICT 2019) in Conjunction with 4th International Symposium on Mobile Internet Security (MobiSec 2019)

The International Symposium on Future ICT (Future-ICT 2019) in conjunction with the 4th International Symposium on Mobile Internet Security (MobiSec 2019) was held on 17–19 October 2019 in Taichung, Taiwan. The symposium provided academic and industry professionals an opportunity to discuss the latest issues and progress in advancing smart applications based on future ICT and its relative security. The symposium aimed to publish high-quality papers strictly related to the various theories and practical applications concerning advanced smart applications, future ICT, and related communications and networks. It was expected that the symposium and its publications would be a trigger for further related research and technology improvements in this field

SIQXC: Schema Independent Queryable XML Compression for Smartphones

The explosive growth of XML use over the last decade has led to a lot of research on how to best store and access it. This growth has resulted in XML being described as a de facto standard for storage and exchange of data over the web. However, XML has high redundancy because of its self-­‐ describing nature making it verbose. The verbose nature of XML poses a storage problem. This has led to much research devoted to XML compression. It has become of more interest since the use of resource constrained devices is also on the rise. These devices are limited in storage space, processing power and also have finite energy. Therefore, these devices cannot cope with storing and processing large XML documents. XML queryable compression methods could be a solution but none of them has a query processor that runs on such devices. Currently, wireless connections are used to alleviate the problem but they have adverse effects on the battery life. They are therefore not a sustainable solution. This thesis describes an attempt to address this problem by proposing a queryable compressor (SIQXC) with a query processor that runs in a resource constrained environment thereby lowering wireless connection dependency yet alleviating the storage problem. It applies a novel simple 2 tuple integer encoding system, clustering and gzip. SIQXC achieves an average compression ratio of 70% which is higher than most queryable XML compressors and also supports a wide range of XPATH operators making it competitive approach. It was tested through a practical implementation evaluated against the real data that is usually used for XML benchmarking. The evaluation covered the compression ratio, compression time and query evaluation accuracy and response time. SIQXC allows users to some extent locally store and manipulate the otherwise verbose XML on their Smartphones

A Partial Read Barrier for Efficient Support of Live Object-oriented Programming

International audienceLive programming, originally introduced by Smalltalk and Lisp, and now gaining popularity in contemporary systems such as Swift, requires on-the-fly support for object schema migration, such that the layout of objects may be changed while the program is at one and the same time being run and developed. In Smalltalk schema migration is supported by two primitives, one that answers a collection of all instances of a class, and one that exchanges the identities of pairs of objects, called the become primitive. Existing instances are collected, copies using the new schema created, state copied from old to new, and the two exchanged with become, effecting the schema migration. Historically the implementation of become has either required an extra level of indirection between an object's address and its body, slowing down slot access, or has required a sweep of all objects, a very slow operation on large heaps. Spur, a new object representation and memory manager for Smalltalk-like languages, has neither of these deficiencies. It uses direct pointers but still provides a fast become operation in large heaps, thanks to forwarding objects that when read conceptually answer another object and a partial read barrier that avoids the cost of explicitly checking for forwarding objects on the vast majority of object accesses

GPU-Acceleration of In-Memory Data Analytics

Hardware advances strongly influence the database system design. The flattening speed of CPU cores makes many-core accelerators, such as GPUs, a vital alternative to explore for processing the ever-increasing amounts of data. GPUs have a significantly higher degree of parallelism than multi-core CPUs but their cores are simpler. As a result, they do not face the power constraints limiting the parallelism of CPUs. Their trade-off, however, is the increased implementation complexity. This thesis adapts and redesigns data analytics operators to better exploit the GPU special memory and threading model. Due to the increasing memory capacity and also the user's need for fast interaction with the data, we focus on in-memory analytics. Our techniques span different steps of the data processing pipeline: (1) Data preprocessing, (2) Query compilation, and (3) Algorithmic optimization of the operators. Our data preprocessing techniques adapt the data layout for numeric and string columns to maximize the achieved GPU memory bandwidth. Our query compilation techniques compute the optimal execution plan for conjunctive filters. We formulate \textit{memory divergence} for string matching algorithms and suggest how to eliminate it. Finally, we parallelize decompression algorithms in our compression framework \textit{Gompresso} to fit more data into the limited GPU memory. Gompresso achieves high speed-ups on GPUs over multi-core CPU state-of-the-art libraries and is suitable for any massively parallel processor