Indexing Highly Repetitive String Collections

Two decades ago, a breakthrough in indexing string collections made it possible to represent them within their compressed space while at the same time offering indexed search functionalities. As this new technology permeated through applications like bioinformatics, the string collections experienced a growth that outperforms Moore's Law and challenges our ability of handling them even in compressed form. It turns out, fortunately, that many of these rapidly growing string collections are highly repetitive, so that their information content is orders of magnitude lower than their plain size. The statistical compression methods used for classical collections, however, are blind to this repetitiveness, and therefore a new set of techniques has been developed in order to properly exploit it. The resulting indexes form a new generation of data structures able to handle the huge repetitive string collections that we are facing. In this survey we cover the algorithmic developments that have led to these data structures. We describe the distinct compression paradigms that have been used to exploit repetitiveness, the fundamental algorithmic ideas that form the base of all the existing indexes, and the various structures that have been proposed, comparing them both in theoretical and practical aspects. We conclude with the current challenges in this fascinating field

Knowledge-Based Techniques for Scholarly Data Access: Towards Automatic Curation

Accessing up-to-date and quality scientific literature is a critical preliminary step in any research activity. Identifying relevant scholarly literature for the extents of a given task or application is, however a complex and time consuming activity. Despite the large number of tools developed over the years to support scholars in their literature surveying activity, such as Google Scholar, Microsoft Academic search, and others, the best way to access quality papers remains asking a domain expert who is actively involved in the field and knows research trends and directions. State of the art systems, in fact, either do not allow exploratory search activity, such as identifying the active research directions within a given topic, or do not offer proactive features, such as content recommendation, which are both critical to researchers. To overcome these limitations, we strongly advocate a paradigm shift in the development of scholarly data access tools: moving from traditional information retrieval and filtering tools towards automated agents able to make sense of the textual content of published papers and therefore monitor the state of the art. Building such a system is however a complex task that implies tackling non trivial problems in the fields of Natural Language Processing, Big Data Analysis, User Modelling, and Information Filtering. In this work, we introduce the concept of Automatic Curator System and present its fundamental components.openDottorato di ricerca in InformaticaopenDe Nart, Dari

X-Stream: Edge-centric Graph Processing using Streaming Partitions

X-Stream is a system for processing both in-memory and out-of-core graphs on a single shared-memory machine. While retaining the scatter-gather programming model with state stored in the vertices, X-Stream is novel in (i) using an edge-centric rather than a vertex-centric implementation of this model, and (ii) streaming completely unordered edge lists rather than performing random access. This design is motivated by the fact that sequential bandwidth for all storage media (main memory, SSD, and magnetic disk) is substantially larger than random access bandwidth. We demonstrate that a large number of graph algorithms can be expressed using the edge-centric scatter-gather model. The resulting implementations scale well in terms of number of cores, in terms of number of I/O devices, and across different storage media. X-Stream competes favorably with existing systems for graph processing. Besides sequential access, we identify as one of the main contributors to better performance the fact that X-Stream does not need to sort edge lists during pre-processing

Creating a Concurrent In-Memory B-Tree Optimized for NUMA Systems

The size of main memory is becoming larger. With the number of Central Processing Unit (CPU) cores ever increasing in modern systems, with each of them being able to access memory, the organization of memory becomes more important. In multicore systems, there are two main architectures for memory organization with respect to the cores - Symmetric Multi-Processor (SMP) and Non-Uniform Memory Architecture (NUMA). Prior work has focused on the improvement of the performance of B-Trees in highly concurrent and distributed environments, as well as in memory, for shared-memory mul- tiprocessors. However, little focus has been given to the performance of main memory B-Trees for NUMA systems. This work focuses on improving the performance of B-Trees contained in main memory of NUMA systems by introducing modifications that consider its storage in the physically distributed main memory of the NUMA system. The work in this thesis makes the following contributions to the development of a distributed B-Tree, specifically in a NUMA environment, modified from a B-Tree originally designed for high concurrency: • It introduces replication of internal nodes of the tree and shows how this can improve its overall performance in a NUMA environment. • It introduces NUMA-aware locking procedures with the aim of managing contention and exploiting locality of lock requests with reference to previous client operation request locations. • It introduces changes in the granularity of locking, starting from the original locking of every node to the locking of certain levels of nodes, showing the tradeoff between the granularity of locking and the performance of the tree based on the workload. • It considers the combination of the different techniques, with the aim of finding the combination which performs well overall for varying read-heavy workloads and number of client threads

Methods for improving entity linking and exploiting social media messages across crises

Entity Linking (EL) is the task of automatically identifying entity mentions in texts and resolving them to a corresponding entity in a reference knowledge base (KB). There is a large number of tools available for different types of documents and domains, however the literature in entity linking has shown the quality of a tool varies across different corpus and depends on specific characteristics of the corpus it is applied to. Moreover the lack of precision on particularly ambiguous mentions often spoils the usefulness of automated disambiguation results in real world applications. In the first part of this thesis I explore an approximation of the difficulty to link entity mentions and frame it as a supervised classification task. Classifying difficult to disambiguate entity mentions can facilitate identifying critical cases as part of a semi-automated system, while detecting latent corpus characteristics that affect the entity linking performance. Moreover, despiteless the large number of entity linking tools that have been proposed throughout the past years, some tools work better on short mentions while others perform better when there is more contextual information. To this end, I proposed a solution by exploiting results from distinct entity linking tools on the same corpus by leveraging their individual strengths on a per-mention basis. The proposed solution demonstrated to be effective and outperformed the individual entity systems employed in a series of experiments. An important component in the majority of the entity linking tools is the probability that a mentions links to one entity in a reference knowledge base, and the computation of this probability is usually done over a static snapshot of a reference KB. However, an entity’s popularity is temporally sensitive and may change due to short term events. Moreover, these changes might be then reflected in a KB and EL tools can produce different results for a given mention at different times. I investigated the prior probability change over time and the overall disambiguation performance using different KB from different time periods. The second part of this thesis is mainly concerned with short texts. Social media has become an integral part of the modern society. Twitter, for instance, is one of the most popular social media platforms around the world that enables people to share their opinions and post short messages about any subject on a daily basis. At first I presented one approach to identifying informative messages during catastrophic events using deep learning techniques. By automatically detecting informative messages posted by users during major events, it can enable professionals involved in crisis management to better estimate damages with only relevant information posted on social media channels, as well as to act immediately. Moreover I have also performed an analysis study on Twitter messages posted during the Covid-19 pandemic. Initially I collected 4 million tweets posted in Portuguese since the begining of the pandemic and provided an analysis of the debate aroud the pandemic. I used topic modeling, sentiment analysis and hashtags recomendation techniques to provide isights around the online discussion of the Covid-19 pandemic

Atti del IX Convegno Annuale dell'Associazione per l'Informatica Umanistica e la Cultura Digitale (AIUCD). La svolta inevitabile: sfide e prospettive per l'Informatica Umanistica

Proceedings of the IX edition of the annual AIUCD conferenc

Atti del IX Convegno Annuale AIUCD. La svolta inevitabile: sfide e prospettive per l'Informatica Umanistica.

La nona edizione del convegno annuale dell'Associazione per l'Informatica Umanistica e la Cultura Digitale (AIUCD 2020; Milano, 15-17 gennaio 2020) ha come tema “La svolta inevitabile: sfide e prospettive per l'Informatica Umanistica”, con lo specifico obiettivo di fornire un'occasione per riflettere sulle conseguenze della crescente diffusione dell’approccio computazionale al trattamento dei dati connessi all’ambito umanistico. Questo volume raccoglie gli articoli i cui contenuti sono stati presentati al convegno. A diversa stregua, essi affrontano il tema proposto da un punto di vista ora più teorico-metodologico, ora più empirico-pratico, presentando i risultati di lavori e progetti (conclusi o in corso) che considerino centrale il trattamento computazionale dei dati