Individuals are generating, storing and accessing more information than ever before. The information comes from a variety of sources such as the World Wide Web, email and books. Storage media is becoming larger and cheaper. This makes accumulation of information easy. When information is kept in large volumes, retrieving it becomes a problem unless there is a system in place for managing this. This study examined the techniques that users have devised to make retrieval of their documents easy and timely. A survey of user document management techniques was done through interviews. The uncovered techniques were then used to build an expert system that provides assistance with document management decision-making. The system provides recommendations on file naming and organization, document backup and archiving as well as suitable storage media. The system poses a series of questions to the user and offers recommendations on the basis of the responses given. The system was evaluated by two categories of users: those who had been interviewed during data collection and those who had not been interviewed. Both categories of users found the recommendations made by the system to be reasonable and indicated that the system was easy to use. Some users thought the system could be of great benefit to people new to computers.

Cloudspeicherdienste sind zu einem Standard für den Austausch großer Datenmengen in virtuellen Gemeinschaften geworden, sowohl im privaten Umfeld als auch im öffentlichen Bereich. Einfache Bedienbarkeit sowie nahtlose Integration in Applikationen, Betriebssystemen und Endgeräten sind wesentliche Bausteine dieses Siegeszuges.

Οι μνήμες τεχνολογίας NAND Flash χρησιμοποιούνται ευρέως για αποθήκευση δεδομένων λόγω της χαρακτηριστικής πυκνότητας, της χαμηλής απαιτούμενης ισχύος, του χαμηλού κόστους, της υψηλής διεκπεραιωτικής ικανότητας και της αξιοπιστίας τους. Η ανάπτυξη της πολυεπίπεδης τεχνολογίας (MLC) έχει καταστήσει δυνατή την αντικατάσταση των σκληρών δίσκων οδήγησης (HDDs) στις φορητές συσκευές και ορισμένους υπολογιστές με NAND μνήμες. Βεβαίως, οι NAND μνήμες δεν διακρίνονται για την απουσία λαθών κατά την αποθήκευση, αλλά στηρίζονται σε τεχνικές διορθώσεις λαθών (ECC) για να επιτύχουν την κατάλληλη αξιοπιστία. Διάφορα φαινόμενα οδηγούν σε λάθη αποθήκευσης στις Flash μνήμες. Σκοπός της παρούσας διπλωματικής εργασίας είναι η ανάλυση αυτών των μηχανισμών εισαγωγής λαθών και η μελέτη από φυσικής πλευράς της τεχνολογίας των MLC NAND Flash μνημών. καθώς και η πειραματική αξιολόγηση τους και η εξαγωγή των αναγκαίων συμπερασμάτων. / --

This is a thesis work performed as a degree completion project in the study program of Bachelors of Science in Electrical Engineering with emphasis on Telecommunication at Blekinge Institute of Technology. The thesis work is based upon the implementation of OpenStack and CDMI (Cloud Data Management Interface) data storage protocols in the web server developed by Compuverde or in any other lightweight, open source and reliable web server. Compuverde is a company, founded by Stefan Bernbo in 2008 in Karlskrona of Sweden, which is aimed to provide data storage solutions and services. This thesis work will help Compuverde to expand its capabilities to serve more clients. The web server developed by Compuverde, in co-ordination with its File System API (application program interface), will enable the clients to use the data storage services and solutions provided by Compuverde by exposing its file system to the clients. OpenStack is an open source cloud computing project, developed by more than 150 companies around the world, which includes an object or data storage system. The feature of the data storage system of OpenStack is that it is not a traditional file system, it is a distributed storage system where greater scalability, redundancy and durability is possible due to lack of central “Brain” or master point. CDMI or Cloud Data Management Interface is a standardized protocol, standardized by SNIA (Storage Networking Industry Association), for self-provisioning, administering and accessing cloud storage. RESTful HTTP operations are defined by CDMI for assessing the capabilities of the cloud storage system, allocating and accessing containers and objects, managing users and groups, implementing access control, attaching metadata, making arbitrary queries, using persistent queues, specifying retention intervals and holds for compliance purposes, using a logging facility, billing, moving data between cloud systems, and exporting data via other protocols such as iSCSI and NFS. Transport security is obtained via TLS. Compuverde did not have the capability to attract clients that use OpenStack and CDMI data storage protocol. To increase its capability, Compuverde decided to develop extended features in an existing lightweight and reliable web server or their own web server, where OpenStack and CDMI are targeted to be enabled. As part of that development this thesis work focuses on implementing a working interface of OpenStack Containers and CDMI Containers and Data Objects at mandatory level. The first phase of the thesis work consists of a research on different lightweight open source web servers like Lighttpd and Nginx, to determine if those web servers fulfill the need of Compuverde or if Compuverde need to create its own web server. The second phase consists of extensive programming in C++ with a purpose to make the chosen web server capable of supporting OpenStack and CDMI data storage features. The C++ programming part of the project is done using Microsoft TeamServer for project portal and source control. The features of OpenStack that are enabled are according to the specifications of “Cloud Files Developer Guide version 1.0” provided by RackSpace Cloud Files while the specification for CDMI is provided SNIA which is named as “Cloud Data Management Interface version 1.0”. / +46760831882

As data and data structures grown more complex in computing, the task of storing and accessing such data efficiently also becomes more complex. Object-oriented programming languages such as Java have popularized the practice of using class-based objects and created the challenge of persisting these complex objects to storage systems such as databases which only store simple scalar values. This paper seeks to explore and compare a selected num-ber of popular persistence solutions for the Java language in their use and per-formance. This is done through researching, implementing and performance testing the chosen candidates. Through these steps we have found that while more abstracted solutions provided easier implementation and usage, these positives come with the disadvantages of generally slower performance. We also discovered that while many persistence frameworks provide features that minimize the amount of code required for implementation, they suffer from performance issues such as the N+1 query issue if not configured or utilized correctly.

Computing systems are becoming increasingly data-intensive because of the explosion of data and the needs for processing the data, and storage management is critical to application performance in such data-intensive computing systems. However, existing resource management frameworks in these systems lack the support for storage management, which causes unpredictable performance degradations when applications are under I/O contention. Storage management of data-intensive systems is a challenging problem because I/O resources cannot be easily partitioned and distributed storage systems require scalable management. This dissertation presents the solutions to address these challenges for typical data-intensive systems including high-performance computing (HPC) systems and big-data systems. For HPC systems, the dissertation presents vPFS, a performance virtualization layer for parallel file system (PFS) based storage systems. It employs user-level PFS proxies to interpose and schedule parallel I/Os on a per-application basis. Based on this framework, it enables SFQ(D)+, a new proportional-share scheduling algorithm which allows diverse applications with good performance isolation and resource utilization. To manage an HPC system’s total I/O service, it also provides two complementary synchronization schemes to coordinate the scheduling of large numbers of storage nodes in a scalable manner. For big-data systems, the dissertation presents IBIS, an interposition-based big-data I/O scheduler. By interposing the different I/O phases of big-data applications, it schedules the I/Os transparently to the applications. It enables a new proportional-share scheduling algorithm, SFQ(D2), to address the dynamics of the underlying storage by adaptively adjusting the I/O concurrency. Moreover, it employs a scalable broker to coordinate the distributed I/O schedulers and provide proportional sharing of a big-data system’s total I/O service. Experimental evaluations show that these solutions have low-overhead and provide strong I/O performance isolation. For example, vPFS’ overhead is less than 3% in through- put and it delivers proportional sharing within 96% of the target for diverse workloads; and IBIS provides up to 99% better performance isolation for WordCount and 30% better proportional slowdown for TeraSort and TeraGen than native YARN.

Patent documents are important intellectual resources of protecting interests of individuals, organizations and companies. These patent documents have great research values, beneficial to the industry, business, law, and policy-making communities. Patent mining aims at assisting patent analysts in investigating, processing, and analyzing patent documents, which has attracted increasing interest in academia and industry. However, despite recent advances in patent mining, several critical issues in current patent mining systems have not been well explored in previous studies. These issues include: 1) the query retrieval problem that assists patent analysts finding all relevant patent documents for a given patent application; 2) the patent documents comparative summarization problem that facilitates patent analysts in quickly reviewing any given patent documents pairs; and 3) the key patent documents discovery problem that helps patent analysts to quickly grasp the linkage between different technologies in order to better understand the technical trend from a collection of patent documents. This dissertation follows the stream of research that covers the aforementioned issues of existing patent analysis and mining systems. In this work, we delve into three interleaved aspects of patent mining techniques, including (1) PatSearch, a framework of automatically generating the search query from a given patent application and retrieving relevant patents to user; (2) PatCom, a framework for investigating the relationship in terms of commonality and difference between patent documents pairs, and (3) PatDom, a framework for integrating multiple types of patent information to identify important patents from a large volume of patent documents. In summary, the increasing amount and textual complexity of patent repository lead to a series of challenges that are not well addressed in the current generation systems. My work proposed reasonable solutions to these challenges and provided insights on how to address these challenges using a simple yet effective integrated patent mining framework.

The cavity supports the orthogonal reference beam families as its eigenmodes while enhancing the reference beam power. Such orthogonal eigenmodes are used as additional degree of freedom to multiplex data pages, consequently increase storage densities for volume Holographic Data Storage Systems (HDSS) when the maximum number of multiplexed data page is limited by geometrical factor. Image bearing holograms are multiplexed by orthogonal phase code multiplexing via Hermite- Gaussian eigenmodes in a Fe: LiNbO3 medium with a 532 nm laser at multiple Bragg angles by using Liquid Crystal on Silicon (LCOS) spatial light modulators (SLMs) in reference arms. Total of nine holograms are recorded with three angular and three eigenmode.

Previously, we proposed and experimentally demonstrated enhanced recording speeds by using a resonant optical cavity to semi-passively increase the reference beam power while recording image bearing holograms. In addition to enhancing the reference beam power the cavity supports the orthogonal reference beam families of its eigenmodes, which can be used as a degree of freedom to multiplex data pages and increase storage densities for volume Holographic Data Storage Systems (HDSS). While keeping the increased recording speed of a cavity enhanced reference arm, image bearing holograms are multiplexed by orthogonal phase code multiplexing via Hermite-Gaussian eigenmodes in a Fe: LiNbO3 medium with a 532 nm laser at two Bragg angles for expedited recording of four multiplexed holograms. We experimentally confirmed write rates are enhanced by an average factor of 1.1, and page crosstalk is about 2.5%. This hybrid multiplexing opens up a pathway to increase storage density while minimizing modifications to current angular multiplexing HDSS.

With the fast growth of data-intensive distributed systems today, more novel and principled approaches are needed to improve the system efficiency, ensure the service quality to satisfy the user requirements, and lower the system running cost. This dissertation studies the design issues in the data-intensive distributed systems, which are differentiated from other systems by the heavy workload of data movement and are characterized by the fact that the destination of each data flow is limited to a subset of available locations, such as those servers holding the requested data. Besides, even among the feasible subset, different locations may result in different performance. The studies in this dissertation improve the data-intensive systems by exploiting the data storage location flexibility. It addresses how to reasonably determine the data placement based on the measured request patterns, to improve a series of performance metrics, such as the data access latency, system throughput and various costs, by the proposed hypergraph models for data placement. To implement the proposal with a lower overhead, a sketch-based data placement scheme is presented, which constructs the sparsified hypergraph under a distributed and streaming-based system model, achieving a good approximation on the performance improvement. As the network can potentially become the bottleneck of distributed data-intensive systems due to the frequent data movement among storage nodes, the online data placement by reinforcement learning is proposed which intelligently determines the storage locations of each data item at the moment that the item is going to be written or updated, with the joint-awareness of network conditions and request patterns. Meanwhile, noticing that distributed memory caches are effective measures in lowering the workload to the backend storage systems, the auto-scaling of memory cache clusters is studied, which tries to balance the energy cost of the service and the performance ensured. As the outcome of this dissertation, the designed schemes and methods essentially help to improve the running efficiency of data-intensive distributed systems. Therefore, they can either help to improve the user-perceived service quality under the same level of system resource investment, or help to lower the monetary expense and energy consumption in maintaining the system under the same performance standard. From the two perspectives, both the end users and the system providers could obtain benefits from the results of the studies. / Graduate