INVISIBLE LABOR FOR DATA: INSTITUTIONS, INFRASTRUCTURE, AND VIRTUAL SPACE

Americans are accustomed to a wide range of data collection in their lives: census, polls, surveys, user registrations, and disclosure forms. When logging onto the Internet, users’ actions are being tracked everywhere: clicking, typing, tapping, swiping, searching, and placing orders. All of this data is stored to create data-driven profiles of each user. Social network sites, furthermore, set the voluntarily sharing of personal data as the default mode of engagement. But people’s time and energy devoted to creating this massive amount of data, on paper and online, are taken for granted. Few people would consider their time and energy spent on data production as labor. Even if some people do acknowledge their labor for data, they believe it is accessory to the activities at hand. In the face of pervasive data collection and the rising time spent on screens, why do people keep ignoring their labor for data? How has labor for data been become invisible, as something that is disregarded by many users? What does invisible labor for data imply for everyday cultural practices in the United States? Invisible Labor for Data addresses these questions. I argue that three intertwined forces contribute to framing data production as being void of labor: data production institutions throughout history, the Internet’s technological infrastructure (especially with the implementation of algorithms), and the multiplication of virtual spaces. There is a common tendency in the framework of human interactions with computers to deprive data and bodies of their materiality. My Introduction and Chapter 1 offer theoretical interventions by reinstating embodied materiality and redefining labor for data as an ongoing process. The middle Chapters present case studies explaining how labor for data is pushed to the margin of the narratives about data production. I focus on a nationwide debate in the 1960s on whether the U.S. should build a databank, contemporary Big Data practices in the data broker and the Internet industries, and the group of people who are hired to produce data for other people’s avatars in the virtual games. I conclude with a discussion on how the new development of crowdsourcing projects may usher in the new chapter in exploiting invisible and discounted labor for data.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

An IP capable data link layer protocol for narrow band half-duplex packet data radio networks

The wide adaptation of Internet Protocol (IP) as de facto protocol for most communication networks has established a need for developing IP capable data link layer protocol solutions for Machine to machine (M2M) and Internet of Things (IoT) networks. However, the wireless networks used for M2M and IoT applications usually lack the resources commonly associated with modern wireless communication networks. The existing IP capable data link layer solutions for wireless IoT networks provide the necessary overhead minimising and frame optimising features, but are often built to be compatible only with IPv6 and specific radio platforms. The objective of this thesis is to design IPv4 compatible data link layer for Netcontrol Oy's narrow band half-duplex packet data radio system. Based on extensive literature research, system modelling and solution concept testing, this thesis proposes the usage of tunslip protocol as the basis for the system data link layer protocol development. In addition to the functionality of tunslip, this thesis discusses the additional network, routing, compression, security and collision avoidance changes required to be made to the radio platform in order for it to be IP compatible while still being able to maintain the point-to-multipoint and multi-hop network characteristics. The data link layer design consists of the radio application, dynamic Maximum Transmission Unit (MTU) optimisation daemon and the tunslip interface. The proposed design uses tunslip for creating an IP capable data link protocol interface. The radio application receives data from tunslip and compresses the packets and uses the IP addressing information for radio network addressing and routing before forwarding the message to radio network. The dynamic MTU size optimisation daemon controls the tunslip interface maximum MTU size according to the link quality assessment calculated from the radio network diagnostic data received from the radio application. For determining the usability of tunslip as the basis for data link layer protocol, testing of the tunslip interface is conducted with both IEEE 802.15.4 radios and packet data radios. The test cases measure the radio network usability for User Datagram Protocol (UDP) based applications without applying any header or content compression. The test results for the packet data radios reveal that the typical success rate for packet reception through a single-hop link is above 99% with a round-trip-delay of 0.315s for 63B packets.

Data-driven Automatic Edge Sharpening

Sharpening is a powerful image transformation because sharp edges can bring out image details. Sharpness is achieved by increasing local contrast and reducing edge widths. We present a method that enhances sharpness of images and thereby their perceptual quality. Most existing enhancement techniques require user input to improve the perception of the scene in a manner most pleasing to the particular user. Our goal of image enhancement is to improve the perception of sharpness in digital images for human viewers. We consider two parameters in order to exaggerate the differences between local intensities. The two parameters exploit local contrast and widths of edges. We start from the assumption that color, texture, or objects of focus such as faces affect the human perception of photographs. When human raters are presented with a collection of images with different sharpness and asked to rank them according to perceived sharpness, the results have shown that there is a statistical consensus among the raters. We introduce a ramp enhancement technique by modifying the optimal overshoot in the ramp for different region contrasts as well as the new ramp width. Optimal parameter values are searched to be applied to regions under the criteria mentioned above. In this way, we aim to enhance digital images automatically to create pleasing image output for common users.

Discovery driven analysis on semi-structured text data

Discovery Driven Analysis (DDA) is a common feature of OLAP technology to analyze structured data. In essence, DDA helps analysts to discover anomalous data by highlighting 'unexpected' values in the OLAP cube. By giving indications to the analyst on what dimensions to explore, DDA speeds up the process of discovering anomalies and their causes. However, Discovery Driven Analysis (and OLAP in general) is only applicable on structured data, such as records in databases. We propose a system to extend DDA technology to semi-structured text documents, that is, text documents with a few structured data. Our system pipeline consists of two stages: first, the text part of each document is structured around user specified dimensions, using semi-PLSA algorithm; then, we adapt DDA to these fully structured documents, thus enabling DDA on text documents. We present some applications of this system in OLAP analysis and show how scalability issues are solved. Results show that our system can handle reasonable datasets of documents, in real time, without any need for pre-computation.

An assessment of data leakage in Firefox under different conditions.

Data leakage is a serious issue and can result in the loss of sensitive data, compromising user accounts and details, potentially affecting millions of internet users. This paper contributes to research in online security and reducing personal footprint by evaluating the levels of privacy provided by the Firefox browser. The aim of identifying conditions that would minimize data leakage and maximize data privacy is addressed by assessing and comparing data leakage in the four possible browsing modes: normal and private modes using a browser installed on the host PC or using a portable browser from a connected USB device respectively. To provide a firm foundation for analysis, a series of carefully designed, pre-planned browsing sessions were repeated in each of the various modes of Firefox. This included low RAM environments to determine any effects low RAM may have on browser data leakage. The results show that considerable data leakage may occur within Firefox. In normal mode, all of the browsing information is stored within the Mozilla profile folder in Firefox-specific SQLite databases and sessionstore.js. While passwords were not stored as plain text, other confidential information such as credit card numbers could be recovered from the Form history under certain conditions. There is no difference when using a portable browser in normal mode, except that the Mozilla profile folder is located on the USB device rather than the host's hard disk. By comparison, private browsing reduces data leakage. Our findings confirm that no information is written to the Firefox-related locations on the hard disk or USB device during private browsing, implying that no deletion would be necessary and no remnants of data would be forensically recoverable from unallocated space. However, two aspects of data leakage occurred equally in all four browsing modes. Firstly, all of the browsing history was stored in the live RAM and was therefore accessible while the browser remained open. Secondly, in low RAM situations, the operating system caches out RAM to pagefile.sys on the host's hard disk. Irrespective of the browsing mode used, this may include Firefox history elements which can then remain forensically recoverable for considerable time.

Top-K Query Processing in Edge-Labeled Graph Data

Edge-labeled graphs have proliferated rapidly over the last decade due to the increased popularity of social networks and the Semantic Web. In social networks, relationships between people are represented by edges and each edge is labeled with a semantic annotation. Hence, a huge single graph can express many different relationships between entities. The Semantic Web represents each single fragment of knowledge as a triple (subject, predicate, object), which is conceptually identical to an edge from subject to object labeled with predicates. A set of triples constitutes an edge-labeled graph on which knowledge inference is performed. Subgraph matching has been extensively used as a query language for patterns in the context of edge-labeled graphs. For example, in social networks, users can specify a subgraph matching query to find all people that have certain neighborhood relationships. Heavily used fragments of the SPARQL query language for the Semantic Web and graph queries of other graph DBMS can also be viewed as subgraph matching over large graphs. Though subgraph matching has been extensively studied as a query paradigm in the Semantic Web and in social networks, a user can get a large number of answers in response to a query. These answers can be shown to the user in accordance with an importance ranking. In this thesis proposal, we present four different scoring models along with scalable algorithms to find the top-k answers via a suite of intelligent pruning techniques. The suggested models consist of a practically important subset of the SPARQL query language augmented with some additional useful features. The first model called Substitution Importance Query (SIQ) identifies the top-k answers whose scores are calculated from matched vertices' properties in each answer in accordance with a user-specified notion of importance. The second model called Vertex Importance Query (VIQ) identifies important vertices in accordance with a user-defined scoring method that builds on top of various subgraphs articulated by the user. Approximate Importance Query (AIQ), our third model, allows partial and inexact matchings and returns top-k of them with a user-specified approximation terms and scoring functions. In the fourth model called Probabilistic Importance Query (PIQ), a query consists of several sub-blocks: one mandatory block that must be mapped and other blocks that can be opportunistically mapped. The probability is calculated from various aspects of answers such as the number of mapped blocks, vertices' properties in each block and so on and the most top-k probable answers are returned. An important distinguishing feature of our work is that we allow the user a huge amount of freedom in specifying: (i) what pattern and approximation he considers important, (ii) how to score answers - irrespective of whether they are vertices or substitution, and (iii) how to combine and aggregate scores generated by multiple patterns and/or multiple substitutions. Because so much power is given to the user, indexing is more challenging than in situations where additional restrictions are imposed on the queries the user can ask. The proposed algorithms for the first model can also be used for answering SPARQL queries with ORDER BY and LIMIT, and the method for the second model also works for SPARQL queries with GROUP BY, ORDER BY and LIMIT. We test our algorithms on multiple real-world graph databases, showing that our algorithms are far more efficient than popular triple stores.

Privacy in data publishing for tailored recommendation scenarios

Personal information is increasingly gathered and used for providing services tailored to user preferences, but the datasets used to provide such functionality can represent serious privacy threats if not appropriately protected. Work in privacy-preserving data publishing targeted privacy guarantees that protect against record re-identification, by making records indistinguishable, or sensitive attribute value disclosure, by introducing diversity or noise in the sensitive values. However, most approaches fail in the high-dimensional case, and the ones that don’t introduce a utility cost incompatible with tailored recommendation scenarios. This paper aims at a sensible trade-off between privacy and the benefits of tailored recommendations, in the context of privacy-preserving data publishing. We empirically demonstrate that significant privacy improvements can be achieved at a utility cost compatible with tailored recommendation scenarios, using a simple partition-based sanitization method.

Hierarchical clustering with multiple-height branch-cut applied to short time-series gene expression data.

Rigid adherence to pre-specified thresholds and static graphical representations can lead to incorrect decisions on merging of clusters. As an alternative to existing automated or semi-automated methods, we developed a visual analytics approach for performing hierarchical clustering analysis of short time-series gene expression data. Dynamic sliders control parameters such as the similarity threshold at which clusters are merged and the level of relative intra-cluster distinctiveness, which can be used to identify "weak-edges" within clusters. An expert user can drill down to further explore the dendrogram and detect nested clusters and outliers. This is done by using the sliders and by pointing and clicking on the representation to cut the branches of the tree in multiple-heights. A prototype of this tool has been developed in collaboration with a small group of biologists for analysing their own datasets. Initial feedback on the tool has been positive.

MLCut: exploring multi-level cuts in dendrograms for biological data.

Choosing a single similarity threshold for cutting dendrograms is not sufficient for performing hierarchical clustering analysis of heterogeneous data sets. In addition, alternative automated or semi-automated methods that cut dendrograms in multiple levels make assumptions about the data in hand. In an attempt to help the user to find patterns in the data and resolve ambiguities in cluster assignments, we developed MLCut: a tool that provides visual support for exploring dendrograms of heterogeneous data sets in different levels of detail. The interactive exploration of the dendrogram is coordinated with a representation of the original data, shown as parallel coordinates. The tool supports three analysis steps. Firstly, a single-height similarity threshold can be applied using a dynamic slider to identify the main clusters. Secondly, a distinctiveness threshold can be applied using a second dynamic slider to identify “weak-edges” that indicate heterogeneity within clusters. Thirdly, the user can drill-down to further explore the dendrogram structure - always in relation to the original data - and cut the branches of the tree at multiple levels. Interactive drill-down is supported using mouse events such as hovering, pointing and clicking on elements of the dendrogram. Two prototypes of this tool have been developed in collaboration with a group of biologists for analysing their own data sets. We found that enabling the users to cut the tree at multiple levels, while viewing the effect in the original data, is a promising method for clustering which could lead to scientific discoveries.

Active Data Collection Techniques to Understand Online Scammers and Cybercriminals

Nigerian scam, also known as advance fee fraud or 419 scam, is a prevalent form of online fraudulent activity that causes financial loss to individuals and businesses. Nigerian scam has evolved from simple non-targeted email messages to more sophisticated scams targeted at users of classifieds, dating and other websites. Even though such scams are observed and reported by users frequently, the community’s understanding of Nigerian scams is limited since the scammers operate “underground”. To better understand the underground Nigerian scam ecosystem and seek effective methods to deter Nigerian scam and cybercrime in general, we conduct a series of active and passive measurement studies. Relying upon the analysis and insight gained from the measurement studies, we make four contributions: (1) we analyze the taxonomy of Nigerian scam and derive long-term trends in scams; (2) we provide an insight on Nigerian scam and cybercrime ecosystems and their underground operation; (3) we propose a payment intervention as a potential deterrent to cybercrime operation in general and evaluate its effectiveness; and (4) we offer active and passive measurement tools and techniques that enable in-depth analysis of cybercrime ecosystems and deterrence on them. We first created and analyze a repository of more than two hundred thousand user-reported scam emails, stretching from 2006 to 2014, from four major scam reporting websites. We select ten most commonly observed scam categories and tag 2,000 scam emails randomly selected from our repository. Based upon the manually tagged dataset, we train a machine learning classifier and cluster all scam emails in the repository. From the clustering result, we find a strong and sustained upward trend for targeted scams and downward trend for non-targeted scams. We then focus on two types of targeted scams: sales scams and rental scams targeted users on Craigslist. We built an automated scam data collection system and gathered large-scale sales scam emails. Using the system we posted honeypot ads on Craigslist and conversed automatically with the scammers. Through the email conversation, the system obtained additional confirmation of likely scam activities and collected additional information such as IP addresses and shipping addresses. Our analysis revealed that around 10 groups were responsible for nearly half of the over 13,000 total scam attempts we received. These groups used IP addresses and shipping addresses in both Nigeria and the U.S. We also crawled rental ads on Craigslist, identified rental scam ads amongst the large number of benign ads and conversed with the potential scammers. Through in-depth analysis of the rental scams, we found seven major scam campaigns employing various operations and monetization methods. We also found that unlike sales scammers, most rental scammers were in the U.S. The large-scale scam data and in-depth analysis provide useful insights on how to design effective deterrence techniques against cybercrime in general. We study underground DDoS-for-hire services, also known as booters, and measure the effectiveness of undermining a payment system of DDoS Services. Our analysis shows that the payment intervention can have the desired effect of limiting cybercriminals’ ability and increasing the risk of accepting payments.

Data warehouse para el Centro de Documentación Regional "Juan Bautista Vázquez"

El proceso de toma de decisiones en las bibliotecas universitarias es de suma importancia, sin embargo, se encuentra complicaciones como la gran cantidad de fuentes de datos y los grandes volúmenes de datos a analizar. Las bibliotecas universitarias están acostumbradas a producir y recopilar una gran cantidad de información sobre sus datos y servicios. Las fuentes de datos comunes son el resultado de sistemas internos, portales y catálogos en línea, evaluaciones de calidad y encuestas. Desafortunadamente estas fuentes de datos sólo se utilizan parcialmente para la toma de decisiones debido a la amplia variedad de formatos y estándares, así como la falta de métodos eficientes y herramientas de integración. Este proyecto de tesis presenta el análisis, diseño e implementación del Data Warehouse, que es un sistema integrado de toma de decisiones para el Centro de Documentación Juan Bautista Vázquez. En primer lugar se presenta los requerimientos y el análisis de los datos en base a una metodología, esta metodología incorpora elementos claves incluyendo el análisis de procesos, la calidad estimada, la información relevante y la interacción con el usuario que influyen en una decisión bibliotecaria. A continuación, se propone la arquitectura y el diseño del Data Warehouse y su respectiva implementación la misma que soporta la integración, procesamiento y el almacenamiento de datos. Finalmente los datos almacenados se analizan a través de herramientas de procesamiento analítico y la aplicación de técnicas de Bibliomining ayudando a los administradores del centro de documentación a tomar decisiones óptimas sobre sus recursos y servicios.

Copernicus. Product User Manual In Situ TAC products

This guide describes the data product files from the Copernicus Marine Environment (CMEMS) In Situ Thematic Centre, what data services are available to access them, and how to use the files and services.

An examination of factors influencing ERP key-user satisfaction: A qualitative case study

Several companies are trying to improve their operation efficiency by implementing an enterprise resource planning (ERP) system that makes it possible to control the resources of the company in real time. However, the success of the implementation project is not a foregone conclusion; a significant part of these projects end in a failure, one way or another. Therefore it is important to investigate ERP system implementation more closely in order to increase understanding about factors influencing ERP system success and to improve the probability of a successful ERP implementation project. Consequently, this study was initiated because a manufacturing case company wanted to review the success of their ERP implementation project. To be exact, the case company hoped to gain both information about the success of the project and insight for future implementation improvement. This study investigated ERP success specifically by examining factors that influence ERP key-user satisfaction. User satisfaction is one of the most commonly applied indicators of information system success. The research data was mainly collected by conducting theme interviews. The subjects of the interviews were six key-users of the newly implemented ERP system. The interviewees were closely involved in the implementation project. Furthermore, they act as representative users that utilize the new system in everyday business processes. The collected data was analyzed by thematizing. Both data collection and analysis were guided by a theoretical frame of reference. This frame was based on previous research on the subject. The results of the study aligned with the theoretical framework to large extent. The four principal factors influencing key-user satisfaction were change management, contractor service, key-user’s system knowledge and characteristics of the ERP product itself. One of the most significant contributions of the research is that it confirmed the existence of a connection between change management and ERP key-user satisfaction. Furthermore, it discovered two new sub-factors influencing contractor service related key-user satisfaction. In addition, the research findings indicated that in order to improve the current level of key-user satisfaction, the case company should pay special attention to system functionality improvement and enhancement of the key-users’ knowledge. During similar implementation projects in the future, it would be important to assure the success of change management and contractor service related processes.

Analysis and Visualisation of Crowd-sourced Tourism Data

C3S2E '16 Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering