Humans Forget, Machines Remember: Artificial Intelligence and the Right to Be Forgotten

To understand the Right to be Forgotten in context of artificial intelligence, it is necessary to first delve into an overview of the concepts of human and AI memory and forgetting. Our current law appears to treat human and machine memory alike – supporting a fictitious understanding of memory and forgetting that does not comport with reality. (Some authors have already highlighted the concerns on the perfect remembering.) This Article will examine the problem of AI memory and the Right to be Forgotten, using this example as a model for understanding the failures of current privacy law to reflect the realities of AI technology. First, this Article analyzes the legal background behind the Right to be Forgotten, in order to understand its potential applicability to AI, including a discussion on the antagonism between the values of privacy and transparency under current E.U. privacy law. Next, the Authors explore whether the Right to be Forgotten is practicable or beneficial in an AI/machine learning context, in order to understand whether and how the law should address the Right to Be Forgotten in a post-AI world. The Authors discuss the technical problems faced when adhering to strict interpretation of data deletion requirements under the Right to be Forgotten, ultimately concluding that it may be impossible to fulfill the legal aims of the Right to be Forgotten in artificial intelligence environments. Finally, this Article addresses the core issue at the heart of the AI and Right to be Forgotten problem: the unfortunate dearth of interdisciplinary scholarship supporting privacy law and regulation

CazDataProvider: a solution to the object-relational mismatch

Dissertação de mestrado em Engenharia de InformáticaToday, most software applications require mechanisms to store information persistently. For decades, Relational Database Management Systems (RDBMSs) have been the most common technology to provide efficient and reliable persistence. Due to the object-relational paradigm mismatch, object oriented applications that store data in relational databases have to deal with Object Relational Mapping (ORM) problems. Since the emerging of new ORM frameworks, there has been an attempt to lure developers for a radical paradigm shift. However, they still often have troubles finding the best persistence mechanism for their applications, especially when they have to bear with legacy database systems. The aim of this dissertation is to discuss the persistence problem on object oriented applications and find the best solutions. The main focus lies on the ORM limitations, patterns, technologies and alternatives. The project supporting this dissertation was implemented at Cachapuz under the Project Global Weighting Solutions (GWS). Essentially, the objectives of GWS were centred on finding the optimal persistence layer for CazFramework, mostly providing database interoperability with close-to-Structured Query Language (SQL) querying. Therefore, this work provides analyses on ORM patterns, frameworks, alternatives to ORM like Object-Oriented Database Management Systems (OODBMSs). It also describes the implementation of CazDataProvider, a .NET library tool providing database interoperability and dynamic query features. In the end, there is a performance comparison of all the technologies debated in this dissertation. The result of this dissertation provides guidance for adopting the best persistence technology or implement the most suitable ORM architectures.Hoje, a maioria dos aplicações requerem mecanismos para armazenar informação persistentemente. Durante décadas, as RDBMSs têm sido a tecnologia mais comum para fornecer persistência eficiente e confiável. Devido à incompatibilidade dos paradigmas objetos-relacional, as aplicações orientadas a objetos que armazenam dados em bases de dados relacionais têm de lidar com os problemas do ORM. Desde o surgimento de novas frameworks ORM, houve uma tentativa de atrair programadores para uma mudança radical de paradigmas. No entanto, eles ainda têm muitas vezes dificuldade em encontrar o melhor mecanismo de persistência para as suas aplicações, especialmente quando eles têm de lidar com bases de dados legadss. O objetivo deste trabalho é discutir o problema de persistência em aplicações orientadas a objetos e encontrar as melhores soluções. O foco principal está nas limitações, padrões e tecnologias do ORM bem como suas alternativas. O projeto de apoio a esta dissertação foi implementado na Cachapuz no âmbito do Projeto GWS. Essencialmente, os objetivos do GWS foram centrados em encontrar a camada de persistência ideal para a CazFramework, principalmente fornecendo interoperabilidade de base de dados e consultas em SQL. Portanto, este trabalho fornece análises sobre padrões, frameworks e alternativas ao ORM como OODBMS. Além disso descreve a implementação do CazDataProvider, uma biblioteca .NET que fornece interoperabilidade de bases de dados e consultas dinâmicas. No final, há uma comparação de desempenho de todas as tecnologias discutidas nesta dissertação. O resultado deste trabalho fornece orientação para adotar a melhor tecnologia de persistência ou implementar as arquiteturas ORM mais adequadas

A Novel, Tag-Based File-System

For decades, computer use has largely focused on managing and manipulating files-- creating and consuming media, browsing the web, software development, and even, with such systems as UNIX and Plan$9$, direct device access can largely be reduced to locating, creating, reading, and writing files. To facilitate these operations, developers have created a vast assortment of file-systems, each presenting a unique framework underlying nearly everything people do with a computer. For various reasons, these file-systems have historically represented only incremental improvements and alterations from their predecessors, leaving the basic design and interaction models relatively unchanged. Because of this, most common file-systems share a similar set of weaknesses and limitations, intrinsic to those models. As an attempt to break with these traditional shortcomings, the author has created STUFFS, a Semantically-Tagged Unstructured Future File-System. It is intended largely as a research platform for investigating fundamentally new ideas in storing, locating, managing, and otherwise manipulating files, their data, and their associated meta-data. As such, STUFFS does not claim to perfectly solve all of these problems -- rather, it serves as a proof-of-concept and testbed for a number of promising new approaches. Of these new features, users are likely most impacted by STUFFS\u27s titular tag-based structure, which spurns the traditional folder hierarchy in favor of a folksonomy inspired, tag-centric approach to file organization. While this change retains backwards compatibility, and is therefore fully usable as a traditional FS, it has profound impact on potential user interaction. In order to support this high level transition, STUFFS is implemented using a relational database for storage and tag-resolution, and, as an exciting side effect, it has gained proper transaction support and full ACID compliance

Letter from the Special Issue Editor

Editorial work for DEBULL on a special issue on data management on Storage Class Memory (SCM) technologies

Data-intensive Systems on Modern Hardware : Leveraging Near-Data Processing to Counter the Growth of Data

Over the last decades, a tremendous change toward using information technology in almost every daily routine of our lives can be perceived in our society, entailing an incredible growth of data collected day-by-day on Web, IoT, and AI applications. At the same time, magneto-mechanical HDDs are being replaced by semiconductor storage such as SSDs, equipped with modern Non-Volatile Memories, like Flash, which yield significantly faster access latencies and higher levels of parallelism. Likewise, the execution speed of processing units increased considerably as nowadays server architectures comprise up to multiple hundreds of independently working CPU cores along with a variety of specialized computing co-processors such as GPUs or FPGAs. However, the burden of moving the continuously growing data to the best fitting processing unit is inherently linked to today’s computer architecture that is based on the data-to-code paradigm. In the light of Amdahl's Law, this leads to the conclusion that even with today's powerful processing units, the speedup of systems is limited since the fraction of parallel work is largely I/O-bound. Therefore, throughout this cumulative dissertation, we investigate the paradigm shift toward code-to-data, formally known as Near-Data Processing (NDP), which relieves the contention on the I/O bus by offloading processing to intelligent computational storage devices, where the data is originally located. Firstly, we identified Native Storage Management as the essential foundation for NDP due to its direct control of physical storage management within the database. Upon this, the interface is extended to propagate address mapping information and to invoke NDP functionality on the storage device. As the former can become very large, we introduce Physical Page Pointers as one novel NDP abstraction for self-contained immutable database objects. Secondly, the on-device navigation and interpretation of data are elaborated. Therefore, we introduce cross-layer Parsers and Accessors as another NDP abstraction that can be executed on the heterogeneous processing capabilities of modern computational storage devices. Thereby, the compute placement and resource configuration per NDP request is identified as a major performance criteria. Our experimental evaluation shows an improvement in the execution durations of 1.4x to 2.7x compared to traditional systems. Moreover, we propose a framework for the automatic generation of Parsers and Accessors on FPGAs to ease their application in NDP. Thirdly, we investigate the interplay of NDP and modern workload characteristics like HTAP. Therefore, we present different offloading models and focus on an intervention-free execution. By propagating the Shared State with the latest modifications of the database to the computational storage device, it is able to process data with transactional guarantees. Thus, we achieve to extend the design space of HTAP with NDP by providing a solution that optimizes for performance isolation, data freshness, and the reduction of data transfers. In contrast to traditional systems, we experience no significant drop in performance when an OLAP query is invoked but a steady and 30% faster throughput. Lastly, in-situ result-set management and consumption as well as NDP pipelines are proposed to achieve flexibility in processing data on heterogeneous hardware. As those produce final and intermediary results, we continue investigating their management and identified that an on-device materialization comes at a low cost but enables novel consumption modes and reuse semantics. Thereby, we achieve significant performance improvements of up to 400x by reusing once materialized results multiple times

A framework for adaptive personalised e-advertisements

The art of personalised e-advertising relies on attracting the user‟s attention to the recommended product, as it relates to their taste, interest and data. Whilst in practice, companies attempt various forms of personalisation; research of personalised e-advertising is rare, and seldom routed on solid theory. Adaptive hypermedia (AH) techniques have contributed to the development of personalised tools for adaptive content delivery, mostly in the educational domain. This study explores the use of these theories and techniques in a specific field – adaptive e-advertisements. This is accomplished firstly by structuring a theoretical framework that roots adaptive hypermedia into the domain of e-advertising and then uses this theoretical framework as the base for implementing and evaluating an adaptive e-advertisement system called “MyAds”. The novelty of this approach relies on a systematic design and evaluation based on adaptive hypermedia taxonomy. In particular, this thesis uses a user centric methodology to design and evaluate the proposed approach. It also reports on evaluations that investigated users‟ opinions on the appropriate design of MyAds. Another set of evaluations reported on users‟ perceptions of the implemented system, allowing for a reflection on the users‟ acceptance level of e-advertising. The results from both implicit and explicit feedback indicated that users found the MyAds system acceptable and agreed that the implemented user modelling and AH features within the system contributed to achieving acceptance, within their e-advertisement experience due to the different personalisation methods

Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data

This book gives a start-to-finish overview of the whole Fish4Knowledge project, in 18 short chapters, each describing one aspect of the project. The Fish4Knowledge project explored the possibilities of big video data, in this case from undersea video. Recording and analyzing 90 thousand hours of video from ten camera locations, the project gives a 3 year view of fish abundance in several tropical coral reefs off the coast of Taiwan. The research system built a remote recording network, over 100 Tb of storage, supercomputer processing, video target detection and