Enterprise search and discovery capability: the factors and generative mechanisms for user satisfaction.

Many organizations are re-creating the 'Google-like' experience behind their firewall to exploit their information. However, surveys show dissatisfaction with enterprise search is commonplace. No prior study has investigated unsolicited user feedback from an enterprise search user interface to understand the underlying reasons for dissatisfaction. A mixed methods longitudinal study was undertaken analysing feedback from over 1,000 users and interviewing search service staff in a multinational corporation. Results show that 62% of dissatisfaction events were due to human (information & search literacy) rather than technology factors. Cognitive biases and the 'Google Habitus' influence expectations and information behaviour, and are postulated as deep underlying generative mechanisms. The current literature focuses on 'structure' (technology and information quality) as the reason for enterprise search satisfaction, agency (search literacy) appears downplayed. Organizations which emphasise 'systems thinking' and bimodal approaches towards search strategy and information behaviour may improve capabilities

Last-Mile TLS Interception: Analysis and Observation of the Non-Public HTTPS Ecosystem

Transport Layer Security (TLS) is one of the most widely deployed cryptographic protocols on the Internet that provides confidentiality, integrity, and a certain degree of authenticity of the communications between clients and servers. Following Snowden's revelations on US surveillance programs, the adoption of TLS has steadily increased. However, encrypted traffic prevents legitimate inspection. Therefore, security solutions such as personal antiviruses and enterprise firewalls may intercept encrypted connections in search for malicious or unauthorized content. Therefore, the end-to-end property of TLS is broken by these TLS proxies (a.k.a. middleboxes) for arguably laudable reasons; yet, may pose a security risk. While TLS clients and servers have been analyzed to some extent, such proxies have remained unexplored until recently. We propose a framework for analyzing client-end TLS proxies, and apply it to 14 consumer antivirus and parental control applications as they break end-to-end TLS connections. Overall, the security of TLS connections was systematically worsened compared to the guarantees provided by modern browsers. Next, we aim at exploring the non-public HTTPS ecosystem, composed of locally-trusted proxy-issued certificates, from the user's perspective and from several countries in residential and enterprise settings. We focus our analysis on the long tail of interception events. We characterize the customers of network appliances, ranging from small/medium businesses and institutes to hospitals, hotels, resorts, insurance companies, and government agencies. We also discover regional cases of traffic interception malware/adware that mostly rely on the same Software Development Kit (i.e., NetFilter). Our scanning and analysis techniques allow us to identify more middleboxes and intercepting apps than previously found from privileged server vantages looking at billions of connections. We further perform a longitudinal study over six years of the evolution of a prominent traffic-intercepting adware found in our dataset: Wajam. We expose the TLS interception techniques it has used and the weaknesses it has introduced on hundreds of millions of user devices. This study also (re)opens the neglected problem of privacy-invasive adware, by showing how adware evolves sometimes stronger than even advanced malware and poses significant detection and reverse-engineering challenges. Overall, whether beneficial or not, TLS interception often has detrimental impacts on security without the end-user being alerted

Big Data for Traffic Monitoring and Management

The last two decades witnessed tremendous advances in the Information and Communications Technologies. Beside improvements in computational power and storage capacity, communication networks carry nowadays an amount of data which was not envisaged only few years ago. Together with their pervasiveness, network complexity increased at the same pace, leaving operators and researchers with few instruments to understand what happens in the networks, and, on the global scale, on the Internet. Fortunately, recent advances in data science and machine learning come to the rescue of network analysts, and allow analyses with a level of complexity and spatial/temporal scope not possible only 10 years ago. In my thesis, I take the perspective of an Internet Service Provider (ISP), and illustrate challenges and possibilities of analyzing the traffic coming from modern operational networks. I make use of big data and machine learning algorithms, and apply them to datasets coming from passive measurements of ISP and University Campus networks. The marriage between data science and network measurements is complicated by the complexity of machine learning algorithms, and by the intrinsic multi-dimensionality and variability of this kind of data. As such, my work proposes and evaluates novel techniques, inspired from popular machine learning approaches, but carefully tailored to operate with network traffic

Business Analytics in the Context of Big Data: A Roadmap for Research

This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems (SIGDSS) workshop, respectively. Recognizing the potential of “big data” to offer new insights for decision making and innovation, panelists at the two events discussed how organizations can use and manage big data for competitive advantage. In addition, expert panelists helped to identify research gaps. While emerging research in the academic community identifies some of the issues in acquiring, analyzing, and using big data, many of the new developments are occurring in the practitioner community. We bridge the gap between academic and practitioner research by presenting a big data analytics framework that depicts a process view of the components needed for big data analytics in organizations. Using practitioner interviews and literature from both academia and practice, we identify the current state of big data research guided by the framework and propose potential areas for future research to increase the relevance of academic research to practice

A User-Centered Approach to Landing Page Optimization in a Software-as-a-Service Business

There are two essential steps in the digital marketing process: acquisition and conversion. Acquisition describes the efforts of getting a potential buyer to visit a business's website. Conversion is concerned with convincing that prospect, who has arrived on a website, to take a desired action, thus to convert. The process of improving conversions is called conversion rate optimization (CRO). While marketers increasingly understand the importance of optimizing their website for conversion, often CRO is only done in a quantitative way, relying on web metrics and visitor behavior. This limited approach does not consider the reasons behind visitors' behavior, their underlying needs and way of thinking when evaluating products and services online. Yet, those reasons are crucial to understand when optimizing for conversion. The objective of this study is to investigate how methods from user-centered design can aid in uncovering the needs and thought process of website visitors evaluating a Software-as-a-Service solution online. Additionally, the visitor's overall buying process is studied. The study is conducted as semi-structured interviews and retrospective testing with six recent website visitors interested in the SaaS service. Thematic analysis and customer journey mapping are used to analyze the interview data. The results indicate that visitor needs are mostly connected to inquiring service-related information, such as performance or features, as well as the pricing range. Additionally, aspects such as ease of getting started, service flexibility and quality support had a strong influence. It was found that most of these aspects are typical for successful SaaS solutions. The overall decision making process of choosing a SaaS solution proved to be fairly unstructured. However, being present in the minds of potential customers before they feel the need to search for solutions actively seems to be crucial in order to be considered. In addition to that, the first impression of a business's online presence also largely impacts visitor trust and consideration. Regarding the final decision making, it is to be noted that technical visitors are strong influencers but the final provider selection is a collaborative effort. Concerning the page itself, visitor conversion is generally favored when presenting relevant content to visitors in relevant order, while leaving out irrelevant content

Analyzing the video popularity characteristics of large-scale user generated content systems

Abstract—User generated content (UGC), now with millions of video producers and consumers, is re-shaping the way people watch video and TV. In particular, UGC sites are creating new viewing patterns and social interactions, empowering users to be more creative, and generating new business opportunities. Compared to traditional video-on-demand (VoD) systems, UGC services allow users to request videos from a potentially unlimited selection in an asynchronous fashion. To better understand the impact of UGC services, we have analyzed the world’s largest UGC VoD system, YouTube, and a popular similar system in Korea, Daum Videos. In this paper, we first empirically show how UGC services are fundamentally different from traditional VoD services. We then analyze the intrinsic statistical properties of UGC popularity distributions and discuss opportunities to leverage the latent demand for niche videos (or the so-called “the Long Tail ” potential), which is not reached today due to informa-tion filtering or other system scarcity distortions. Based on traces collected across multiple days, we study the popularity lifetime of UGC videos and the relationship between requests and video age. Finally, we measure the level of content aliasing and illegal content in the system and show the problems aliasing creates in ranking the video popularity accurately. The results presented in this paper are crucial to understanding UGC VoD systems and may have major commercial and technical implications for site administrators and content owners. Index Terms—Interactive TV, human factors, exponential distri-butions, log normal distributions, pareto distributions, probability, copyright protection. I