A Review and Characterization of Progressive Visual Analytics

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions

Flow-based reputation with uncertainty: Evidence-Based Subjective Logic

The concept of reputation is widely used as a measure of trustworthiness based on ratings from members in a community. The adoption of reputation systems, however, relies on their ability to capture the actual trustworthiness of a target. Several reputation models for aggregating trust information have been proposed in the literature. The choice of model has an impact on the reliability of the aggregated trust information as well as on the procedure used to compute reputations. Two prominent models are flow-based reputation (e.g., EigenTrust, PageRank) and Subjective Logic based reputation. Flow-based models provide an automated method to aggregate trust information, but they are not able to express the level of uncertainty in the information. In contrast, Subjective Logic extends probabilistic models with an explicit notion of uncertainty, but the calculation of reputation depends on the structure of the trust network and often requires information to be discarded. These are severe drawbacks. In this work, we observe that the `opinion discounting' operation in Subjective Logic has a number of basic problems. We resolve these problems by providing a new discounting operator that describes the flow of evidence from one party to another. The adoption of our discounting rule results in a consistent Subjective Logic algebra that is entirely based on the handling of evidence. We show that the new algebra enables the construction of an automated reputation assessment procedure for arbitrary trust networks, where the calculation no longer depends on the structure of the network, and does not need to throw away any information. Thus, we obtain the best of both worlds: flow-based reputation and consistent handling of uncertainties

An effective recommender system by unifying user and item trust information for B2B applications

© 2015 Elsevier Inc. Although Collaborative Filtering (CF)-based recommender systems have received great success in a variety of applications, they still under-perform and are unable to provide accurate recommendations when users and items have few ratings, resulting in reduced coverage. To overcome these limitations, we propose an effective hybrid user-item trust-based (HUIT) recommendation approach in this paper that fuses the users' and items' implicit trust information. We have also considered and computed user and item global reputations into this approach. This approach allows the recommender system to make an increased number of accurate predictions, especially in circumstances where users and items have few ratings. Experiments on four real-world datasets, particularly a business-to-business (B2B) case study, show that the proposed HUIT recommendation approach significantly outperforms state-of-the-art recommendation algorithms in terms of recommendation accuracy and coverage, as well as significantly alleviating data sparsity, cold-start user and cold-start item problems

Trustworthiness Management in the Internet of Things

The future Internet of Things (IoT) will be characterized by an increasing number of object-to-object interactions for the implementation of distributed applications running in smart environments. Object cooperation allows us to develop complex applications in which each node contributes one or more services. The Social IoT (SIoT) is one of the possible paradigms that is proposed to make the objects’ interactions easier by facilitating the search for services and the management of objects’ trustworthiness. In that scenario, in which the information moves from a provider to a requester node in a peer-to-peer network, Trust Management Systems (TMSs) have been developed to prevent the manipulation of data by unauthorized entities and guarantee the detection of malicious behaviour. The cornerstone of any TMS is the ability to generate a coherent evaluation of the information received. The community concentrates effort on designing complex trust techniques to increase their effectiveness; however, strong assumptions still need to be considered. First, nodes could provide the wrong services due to malicious behaviours or malfunctions and insufficient accuracy. Second, the requester nodes usually cannot evaluate the received service perfectly. In this regard, this thesis proposes an exhaustive analysis of the trustworthiness management in the IoT and SIoT. To this, in the beginning, we generate a dataset and propose a query generation model, essential to develop a first trust management model to overcome all the attacks in the literature. So, we implement several trust mechanisms and identify the importance of the overlooked assumptions in different scenarios. Then, to solve the issues, we concentrate on the generation of feedback and propose different metrics to evaluate it based on the presence or not of errors. Finally, we focus on modelling the interaction between the two figures involved in interactions, i.e. the trustor and the trustee, and on proposing guidelines to efficiently design trust management models

A Trust Management Framework for Decision Support Systems

In the era of information explosion, it is critical to develop a framework which can extract useful information and help people to make “educated” decisions. In our lives, whether we are aware of it, trust has turned out to be very helpful for us to make decisions. At the same time, cognitive trust, especially in large systems, such as Facebook, Twitter, and so on, needs support from computer systems. Therefore, we need a framework that can effectively, but also intuitively, let people express their trust, and enable the system to automatically and securely summarize the massive amounts of trust information, so that a user of the system can make “educated” decisions, or at least not blind decisions. Inspired by the similarities between human trust and physical measurements, this dissertation proposes a measurement theory based trust management framework. It consists of three phases: trust modeling, trust inference, and decision making. Instead of proposing specific trust inference formulas, this dissertation proposes a fundamental framework which is flexible and can be adapted by many different inference formulas. Validation experiments are done on two data sets: the Epinions.com data set and the Twitter data set. This dissertation also adapts the measurement theory based trust management framework for two decision support applications. In the first application, the real stock market data is used as ground truth for the measurement theory based trust management framework. Basically, the correlation between the sentiment expressed on Twitter and stock market data is measured. Compared with existing works which do not differentiate tweets’ authors, this dissertation analyzes trust among stock investors on Twitter and uses the trust network to differentiate tweets’ authors. The results show that by using the measurement theory based trust framework, Twitter sentiment valence is able to reflect abnormal stock returns better than treating all the authors as equally important or weighting them by their number of followers. In the second application, the measurement theory based trust management framework is used to help to detect and prevent from being attacked in cloud computing scenarios. In this application, each single flow is treated as a measurement. The simulation results show that the measurement theory based trust management framework is able to provide guidance for cloud administrators and customers to make decisions, e.g. migrating tasks from suspect nodes to trustworthy nodes, dynamically allocating resources according to trust information, and managing the trade-off between the degree of redundancy and the cost of resources

대용량 데이터 탐색을 위한 점진적 시각화 시스템 설계

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 서진욱.Understanding data through interactive visualization, also known as visual analytics, is a common and necessary practice in modern data science. However, as data sizes have increased at unprecedented rates, the computation latency of visualization systems becomes a significant hurdle to visual analytics. The goal of this dissertation is to design a series of systems for progressive visual analytics (PVA)—a visual analytics paradigm that can provide intermediate results during computation and allow visual exploration of these results—to address the scalability hurdle. To support the interactive exploration of data with billions of records, we first introduce SwiftTuna, an interactive visualization system with scalable visualization and computation components. Our performance benchmark demonstrates that it can handle data with four billion records, giving responsive feedback every few seconds without precomputation. Second, we present PANENE, a progressive algorithm for the Approximate k-Nearest Neighbor (AKNN) problem. PANENE brings useful machine learning methods into visual analytics, which has been challenging due to their long initial latency resulting from AKNN computation. In particular, we accelerate t-Distributed Stochastic Neighbor Embedding (t-SNE), a popular non-linear dimensionality reduction technique, which enables the responsive visualization of data with a few hundred columns. Each of these two contributions aims to address the scalability issues stemming from a large number of rows or columns in data, respectively. Third, from the users' perspective, we focus on improving the trustworthiness of intermediate knowledge gained from uncertain results in PVA. We propose a novel PVA concept, Progressive Visual Analytics with Safeguards, and introduce PVA-Guards, safeguards people can leave on uncertain intermediate knowledge that needs to be verified. We also present a proof-of-concept system, ProReveal, designed and developed to integrate seven safeguards into progressive data exploration. Our user study demonstrates that people not only successfully created PVA-Guards on ProReveal but also voluntarily used PVA-Guards to manage the uncertainty of their knowledge. Finally, summarizing the three studies, we discuss design challenges for progressive systems as well as future research agendas for PVA.현대 데이터 사이언스에서 인터랙티브한 시각화를 통해 데이터를 이해하는 것은 필수적인 분석 방법 중 하나이다. 그러나, 최근 데이터의 크기가 폭발적으로 증가하면서 데이터 크기로 인해 발생하는 지연 시간이 인터랙티브한 시각적 분석에 큰 걸림돌이 되었다. 본 연구에서는 이러한 확장성 문제를 해결하기 위해 점진적 시각적 분석(Progressive Visual Analytics)을 지원하는 일련의 시스템을 디자인하고 개발한다. 이러한 점진적 시각적 분석 시스템은 데이터 처리가 완전히 끝나지 않더라도 중간 분석 결과를 사용자에게 제공함으로써 데이터의 크기로 인해 발생하는 지연 시간 문제를 완화할 수 있다. 첫째로, 수십억 건의 행을 가지는 데이터를 시각적으로 탐색할 수 있는 SwiftTuna 시스템을 제안한다. 데이터 처리 및 시각적 표현의 확장성을 목표로 개발된 이 시스템은, 약 40억 건의 행을 가진 데이터에 대한 시각화를 전처리 없이 수 초마다 업데이트할 수 있는 것으로 나타났다. 둘째로, 근사적 k-최근접점(Approximate k-Nearest Neighbor) 문제를 점진적으로 계산하는 PANENE 알고리즘을 제안한다. 근사적 k-최근접점 문제는 여러 기계 학습 기법에서 쓰임에도 불구하고 초기 계산 시간이 길어서 인터랙티브한 시스템에 적용하기 힘든 한계가 있었다. PANENE 알고리즘은 이러한 긴 초기 계산 시간을 획기적으로 개선하여 다양한 기계 학습 기법을 시각적 분석에 활용할 수 있도록 한다. 특히, 유용한 비선형적 차원 감소 기법인 t-분포 확률적 임베딩(t-Distributed Stochastic Neighbor Embedding)을 가속하여 수백 개의 차원을 가지는 데이터를 빠른 시간 내에 사영할 수 있다. 위의 두 시스템과 알고리즘이 데이터의 행 또는 열의 개수로 인한 확장성 문제를 해결하고자 했다면, 세 번째 시스템에서는 점진적 시각적 분석의 신뢰도 문제를 개선하고자 한다. 점진적 시각적 분석에서 사용자에게 주어지는 중간 계산 결과는 최종 결과의 근사치이므로 불확실성이 존재한다. 본 연구에서는 세이프가드를 이용한 점진적 시각적 분석(Progressive Visual Analytics with Safeguards)이라는 새로운 개념을 제안한다. 이 개념은 사용자가 점진적 탐색에서 마주하는 불확실한 중간 지식에 세이프가드를 남길 수 있도록 하여 탐색에서 얻은 지식의 정확도를 추후 검증할 수 있도록 한다. 또한, 이러한 개념을 실제로 구현하여 탑재한 ProReveal 시스템을 소개한다. ProReveal를 이용한 사용자 실험에서 사용자들은 세이프가드를 성공적으로 만들 수 있었을 뿐만 아니라, 중간 지식의 불확실성을 다루기 위해 세이프가드를 자발적으로 이용한다는 것을 알 수 있었다. 마지막으로, 위 세 가지 연구의 결과를 종합하여 점진적 시각적 분석 시스템을 구현할 때의 디자인적 난제와 향후 연구 방향을 모색한다.CHAPTER1. Introduction 2 1.1 Background and Motivation 2 1.2 Thesis Statement and Research Questions 5 1.3 Thesis Contributions 5 1.3.1 Responsive and Incremental Visual Exploration of Large-scale Multidimensional Data 6 1.3.2 ProgressiveComputation of Approximate k-Nearest Neighbors and Responsive t-SNE 7 1.3.3 Progressive Visual Analytics with Safeguards 8 1.4 Structure of Dissertation 9 CHAPTER2. Related Work 11 2.1 Progressive Visual Analytics 11 2.1.1 Definitions 11 2.1.2 System Latency and Human Factors 13 2.1.3 Users, Tasks, and Models 15 2.1.4 Techniques, Algorithms, and Systems. 17 2.1.5 Uncertainty Visualization 19 2.2 Approaches for Scalable Visualization Systems 20 2.3 The k-Nearest Neighbor (KNN) Problem 22 2.4 t-Distributed Stochastic Neighbor Embedding 26 CHAPTER3. SwiTuna: Responsive and Incremental Visual Exploration of Large-scale Multidimensional Data 28 3.1 The SwiTuna Design 31 3.1.1 Design Considerations 32 3.1.2 System Overview 33 3.1.3 Scalable Visualization Components 36 3.1.4 Visualization Cards 40 3.1.5 User Interface and Interaction 42 3.2 Responsive Querying 44 3.2.1 Querying Pipeline 44 3.2.2 Prompt Responses 47 3.2.3 Incremental Processing 47 3.3 Evaluation: Performance Benchmark 49 3.3.1 Study Design 49 3.3.2 Results and Discussion 52 3.4 Implementation 56 3.5 Summary 56 CHAPTER4. PANENE:AProgressive Algorithm for IndexingandQuerying Approximate k-Nearest Neighbors 58 4.1 Approximate k-Nearest Neighbor 61 4.1.1 A Sequential Algorithm 62 4.1.2 An Online Algorithm 63 4.1.3 A Progressive Algorithm 66 4.1.4 Filtered AKNN Search 71 4.2 k-Nearest Neighbor Lookup Table 72 4.3 Benchmark. 78 4.3.1 Online and Progressive k-d Trees 78 4.3.2 k-Nearest Neighbor Lookup Tables 83 4.4 Applications 85 4.4.1 Progressive Regression and Density Estimation 85 4.4.2 Responsive t-SNE 87 4.5 Implementation 92 4.6 Discussion 92 4.7 Summary 93 CHAPTER5. ProReveal: Progressive Visual Analytics with Safeguards 95 5.1 Progressive Visual Analytics with Safeguards 98 5.1.1 Definition 98 5.1.2 Examples 101 5.1.3 Design Considerations 103 5.2 ProReveal 105 5.3 Evaluation 121 5.4 Discussion 127 5.5 Summary 130 CHAPTER6. Discussion 132 6.1 Lessons Learned 132 6.2 Limitations 135 CHAPTER7. Conclusion 137 7.1 Thesis Contributions Revisited 137 7.2 Future Research Agenda 139 7.3 Final Remarks 141 Abstract (Korean) 155 Acknowledgments (Korean) 157Docto

Credibility analysis of textual claims with explainable evidence

Despite being a vast resource of valuable information, the Web has been polluted by the spread of false claims. Increasing hoaxes, fake news, and misleading information on the Web have given rise to many fact-checking websites that manually assess these doubtful claims. However, the rapid speed and large scale of misinformation spread have become the bottleneck for manual verification. This calls for credibility assessment tools that can automate this verification process. Prior works in this domain make strong assumptions about the structure of the claims and the communities where they are made. Most importantly, black-box techniques proposed in prior works lack the ability to explain why a certain statement is deemed credible or not. To address these limitations, this dissertation proposes a general framework for automated credibility assessment that does not make any assumption about the structure or origin of the claims. Specifically, we propose a feature-based model, which automatically retrieves relevant articles about the given claim and assesses its credibility by capturing the mutual interaction between the language style of the relevant articles, their stance towards the claim, and the trustworthiness of the underlying web sources. We further enhance our credibility assessment approach and propose a neural-network-based model. Unlike the feature-based model, this model does not rely on feature engineering and external lexicons. Both our models make their assessments interpretable by extracting explainable evidence from judiciously selected web sources. We utilize our models and develop a Web interface, CredEye, which enables users to automatically assess the credibility of a textual claim and dissect into the assessment by browsing through judiciously and automatically selected evidence snippets. In addition, we study the problem of stance classification and propose a neural-network-based model for predicting the stance of diverse user perspectives regarding the controversial claims. Given a controversial claim and a user comment, our stance classification model predicts whether the user comment is supporting or opposing the claim.Das Web ist eine riesige Quelle wertvoller Informationen, allerdings wurde es durch die Verbreitung von Falschmeldungen verschmutzt. Eine zunehmende Anzahl an Hoaxes, Falschmeldungen und irreführenden Informationen im Internet haben viele Websites hervorgebracht, auf denen die Fakten überprüft und zweifelhafte Behauptungen manuell bewertet werden. Die rasante Verbreitung großer Mengen von Fehlinformationen sind jedoch zum Engpass für die manuelle Überprüfung geworden. Dies erfordert Tools zur Bewertung der Glaubwürdigkeit, mit denen dieser Überprüfungsprozess automatisiert werden kann. In früheren Arbeiten in diesem Bereich werden starke Annahmen gemacht über die Struktur der Behauptungen und die Portale, in denen sie gepostet werden. Vor allem aber können die Black-Box-Techniken, die in früheren Arbeiten vorgeschlagen wurden, nicht erklären, warum eine bestimmte Aussage als glaubwürdig erachtet wird oder nicht. Um diesen Einschränkungen zu begegnen, wird in dieser Dissertation ein allgemeines Framework für die automatisierte Bewertung der Glaubwürdigkeit vorgeschlagen, bei dem keine Annahmen über die Struktur oder den Ursprung der Behauptungen gemacht werden. Insbesondere schlagen wir ein featurebasiertes Modell vor, das automatisch relevante Artikel zu einer bestimmten Behauptung abruft und deren Glaubwürdigkeit bewertet, indem die gegenseitige Interaktion zwischen dem Sprachstil der relevanten Artikel, ihre Haltung zur Behauptung und der Vertrauenswürdigkeit der zugrunde liegenden Quellen erfasst wird. Wir verbessern unseren Ansatz zur Bewertung der Glaubwürdigkeit weiter und schlagen ein auf neuronalen Netzen basierendes Modell vor. Im Gegensatz zum featurebasierten Modell ist dieses Modell nicht auf Feature-Engineering und externe Lexika angewiesen. Unsere beiden Modelle machen ihre Einschätzungen interpretierbar, indem sie erklärbare Beweise aus sorgfältig ausgewählten Webquellen extrahieren. Wir verwenden unsere Modelle zur Entwicklung eines Webinterfaces, CredEye, mit dem Benutzer die Glaubwürdigkeit einer Behauptung in Textform automatisch bewerten und verstehen können, indem sie automatisch ausgewählte Beweisstücke einsehen. Darüber hinaus untersuchen wir das Problem der Positionsklassifizierung und schlagen ein auf neuronalen Netzen basierendes Modell vor, um die Position verschiedener Benutzerperspektiven in Bezug auf die umstrittenen Behauptungen vorherzusagen. Bei einer kontroversen Behauptung und einem Benutzerkommentar sagt unser Einstufungsmodell voraus, ob der Benutzerkommentar die Behauptung unterstützt oder ablehnt

Decision support for choice of security solution: the Aspect-Oriented Risk Driven Development (AORDD)framework

In security assessment and management there is no single correct solution to the identified security problems or challenges. Instead there are only choices and tradeoffs. The main reason for this is that modern information systems and security critical information systems in particular must perform at the contracted or expected security level, make effective use of available resources and meet end-users' expectations. Balancing these needs while also fulfilling development, project and financial perspectives, such as budget and TTM constraints, mean that decision makers have to evaluate alternative security solutions.\ud \ud This work describes parts of an approach that supports decision makers in choosing one or a set of security solutions among alternatives. The approach is called the Aspect-Oriented Risk Driven Development (AORDD) framework, combines Aspect-Oriented Modeling (AOM) and Risk Driven Development (RDD) techniques and consists of the seven components: (1) An iterative AORDD process. (2) Security solution aspect repository. (3) Estimation repository to store experience from estimation of security risks and security solution variables involved in security solution decisions. (4) RDD annotation rules for security risk and security solution variable estimation. (5) The AORDD security solution trade-off analysis and trade-o¤ tool BBN topology. (6) Rule set for how to transfer RDD information from the annotated UML diagrams into the trad-off tool BBN topology. (7) Trust-based information aggregation schema to aggregate disparate information in the trade-o¤ tool BBN topology. This work focuses on components 5 and 7, which are the two core components in the AORDD framework