The Application of Deep Learning and Cloud Technologies to Data Science

Machine Learning and Cloud Computing have become a staple to businesses and educational institutions over the recent years. The two forefronts of big data solutions have garnered technology giants to race for the superior implementation of both Machine Learning and Cloud Computing. The objective of this thesis is to test and utilize AWS SageMaker in three different applications: time-series forecasting with sentiment analysis, automated Machine Learning (AutoML), and finally anomaly detection. The first study covered is a sentiment-based LSTM for stock price prediction. The LSTM was created with two methods, the first being SQL Server Data Tools, and the second being an implementation of LSTM using the Keras library. These results were then evaluated using accuracy, precision, recall, f-1 score, mean absolute error (MAE), root mean squared error (RMSE), and symmetric mean absolute percentage error (SMAPE). The results of this project were that the sentiment models all outperformed the control LSTM. The public model for Facebook on SQL Server Data Tools performed the best overall with 0.9743 accuracy and 0.9940 precision. The second study covered is an application of AWS SageMaker AutoPilot which is an AutoML platform designed to make Machine Learning more accessible to those without programming backgrounds. The methodology of this study follows the application of AWS Data Wrangler and AutoPilot from beginning of the process to completion. The results were evaluated using the metrics of: accuracy, precision, recall, and f-1 score. The best accuracy is given to the LightGBM model on the AI4I Maintenance dataset with an accuracy of 0.983. This model also scored the best on precision, recall, and F1 Score. The final study covered is an anomaly detection system for cyber security intrusion detection system data. The Intrusion Detection Systems that have been rule based are able to catch most of the cyber threats that are prevalent in network traffic; however, the copious amounts of alerts are nearly impossible for humans to keep up with. The methodology of this study follows a typical taxonomy of: data collection, data processing, model creation, and model evaluation. Both Random Cut Forest and XGBoost are implemented using AWS SageMaker. The Supervised Learning Algorithm of XGBoost was able to have the highest accuracy of all models with Model 2 giving an accuracy of 0.6183. This model also showed a Precision of 0.5902, Recall of 0.9649, and F1 Score 0.7324

Proceedings of the 12th International Conference on Digital Preservation

The 12th International Conference on Digital Preservation (iPRES) was held on November 2-6, 2015 in Chapel Hill, North Carolina, USA. There were 327 delegates from 22 countries. The program included 12 long papers, 15 short papers, 33 posters, 3 demos, 6 workshops, 3 tutorials and 5 panels, as well as several interactive sessions and a Digital Preservation Showcase

Proceedings of the 12th International Conference on Digital Preservation

The 12th International Conference on Digital Preservation (iPRES) was held on November 2-6, 2015 in Chapel Hill, North Carolina, USA. There were 327 delegates from 22 countries. The program included 12 long papers, 15 short papers, 33 posters, 3 demos, 6 workshops, 3 tutorials and 5 panels, as well as several interactive sessions and a Digital Preservation Showcase

Computing Competencies for Undergraduate Data Science Curricula: ACM Data Science Task Force

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level. There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science programs, employers who hope to hire a workforce with knowledge and experience in data science, as well as individuals and professional societies representing the fields of computing, statistics, machine learning, computational biology, computational social sciences, digital humanities, and others. There is a shared desire to form a broad interdisciplinary definition of data science and to develop curriculum guidance for degree programs in data science. This volume builds upon the important work of other groups who have published guidelines for data science education. There is a need to acknowledge the definition and description of the individual contributions to this interdisciplinary field. For instance, those interested in the business context for these concepts generally use the term “analytics”; in some cases, the abbreviation DSA appears, meaning Data Science and Analytics. This volume is the third draft articulation of computing-focused competencies for data science. It recognizes the inherent interdisciplinarity of data science and situates computing-specific competencies within the broader interdisciplinary space

Heterogeneous data to knowledge graphs matching

Many applications rely on the existence of reusable data. The FAIR (Findability, Accessibility, Interoperability, and Reusability) principles identify detailed descriptions of data and metadata as the core ingredients for achieving reusability. However, creating descriptive data requires massive manual effort. One way to ensure that data is reusable is by integrating it into Knowledge Graphs (KGs). The semantic foundation of these graphs provides the necessary description for reuse. In the Open Research KG, they propose to model artifacts of scientific endeavors, including publications and their key messages. Datasets supporting these publications are essential carriers of scientific knowledge and should be included in KGs. We focus on biodiversity research as an example domain to develop and evaluate our approach. Biodiversity is the assortment of life on earth covering evolutionary, ecological, biological, and social forms. Understanding such a domain and its mechanisms is essential to preserving this vital foundation of human well-being. It is imperative to monitor the current state of biodiversity and its change over time and to understand its forces driving and preserving life in all its variety and richness. This need has resulted in numerous works being published in this field. For example, a large amount of tabular data (datasets), textual data (publications), and metadata (e.g., dataset description) have been generated. So, it is a data-rich domain with an exceptionally high need for data reuse. Managing and integrating these heterogeneous data of biodiversity research remains a big challenge. Our core research problem is how to enable the reusability of tabular data, which is one aspect of the FAIR data principles. In this thesis, we provide answer for this research problem

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Web-scale web table to knowledge base matching

Millions of relational HTML tables are found on the World Wide Web. In contrast to unstructured text, relational web tables provide a compact representation of entities described by attributes. The data within these tables covers a broad topical range. Web table data is used for question answering, augmentation of search results, and knowledge base completion. Until a few years ago, only search engines companies like Google and Microsoft owned large web crawls from which web tables are extracted. Thus, researches outside the companies have not been able to work with web tables. In this thesis, the first publicly available web table corpus containing millions of web tables is introduced. The corpus enables interested researchers to experiment with web tables. A profile of the corpus is created to give insights to the characteristics and topics. Further, the potential of web tables for augmenting cross-domain knowledge bases is investigated. For the use case of knowledge base augmentation, it is necessary to understand the web table content. For this reason, web tables are matched to a knowledge base. The matching comprises three matching tasks: instance, property, and class matching. Existing web table to knowledge base matching systems either focus on a subset of these matching tasks or are evaluated using gold standards which also only cover a subset of the challenges that arise when matching web tables to knowledge bases. This thesis systematically evaluates the utility of a wide range of different features for the web table to knowledge base matching task using a single gold standard. The results of the evaluation are used afterwards to design a holistic matching method which covers all matching tasks and outperforms state-of-the-art web table to knowledge base matching systems. In order to achieve these goals, we first propose the T2K Match algorithm which addresses all three matching tasks in an integrated fashion. In addition, we introduce the T2D gold standard which covers a wide variety of challenges. By evaluating T2K Match against the T2D gold standard, we identify that only considering the table content is insufficient. Hence, we include features of three categories: features found in the table, in the table context like the page title, and features that base on external resources like a synonym dictionary. We analyze the utility of the features for each matching task. The analysis shows that certain problems cannot be overcome by matching each table in isolation to the knowledge base. In addition, relying on the features is not enough for the property matching task. Based on these findings, we extend T2K Match into T2K Match++ which exploits indirect matches to web tables about the same topic and uses knowledge derived from the knowledge base. We show that T2K Match++ outperforms all state-of-the-art web table to knowledge base matching approaches on the T2D and Limaye gold standard. Most systems show good results on one matching task but T2K Match++ is the only system that achieves F-measure scores above 0:8 for all tasks. Compared to results of the best performing system TableMiner+, the F-measure for the difficult property matching task is increased by 0.08, for the class and instance matching task by 0.05 and 0.03, respectively

Mixed Spatial and Nonspatial Problems in Location Based Services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe\u27s ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp