Yavaa: supporting data workflows from discovery to visualization

Recent years have witness an increasing number of data silos being opened up both within organizations and to the general public: Scientists publish their raw data as supplements to articles or even standalone artifacts to enable others to verify and extend their work. Governments pass laws to open up formerly protected data treasures to improve accountability and transparency as well as to enable new business ideas based on this public good. Even companies share structured information about their products and services to advertise their use and thus increase revenue. Exploiting this wealth of information holds many challenges for users, though. Oftentimes data is provided as tables whose sheer endless rows of daunting numbers are barely accessible. InfoVis can mitigate this gap. However, offered visualization options are generally very limited and next to no support is given in applying any of them. The same holds true for data wrangling. Only very few options to adjust the data to the current needs and barely any protection are in place to prevent even the most obvious mistakes. When it comes to data from multiple providers, the situation gets even bleaker. Only recently tools emerged to search for datasets across institutional borders reasonably. Easy-to-use ways to combine these datasets are still missing, though. Finally, results generally lack proper documentation of their provenance. So even the most compelling visualizations can be called into question when their coming about remains unclear. The foundations for a vivid exchange and exploitation of open data are set, but the barrier of entry remains relatively high, especially for non-expert users. This thesis aims to lower that barrier by providing tools and assistance, reducing the amount of prior experience and skills required. It covers the whole workflow ranging from identifying proper datasets, over possible transformations, up until the export of the result in the form of suitable visualizations

An Online Analytical System for Multi-Tagged Document Collections

The New York Times Annotated Corpus and the ACM Digital Library are two prototypical examples of document collections in which each document is tagged with keywords and significant phrases. Such collections can be viewed as high-dimensional document cubes against which browsers and search systems can be applied in a manner similar to online analytical processing against data cubes. The tagging patterns in these collections are examined and a generative tagging model is developed that can mimic the tag assignments observed in those collections. When a user browses the collection by means of a Boolean query over tags, the result is a subset of documents that can be summarized by a centroid derived from their document term vectors. A partial materialization strategy is developed to provide efficient storage and access to centroids for such document subsets. A customized local term vocabulary storage approach is incorporated into the partial materialization to ensure that rich and relevant term vocabulary is available for representing centroids while maintaining a low storage footprint. By adopting this strategy, summary measures dependent on centroids (including bursty terms, or larger sets of indicative documents) can be efficiently and accurately computed for important subsets of documents. The proposed design is evaluated on the two collections along with PubMed (a held-back document collection) and several synthetic collections to validate that it outperforms alternative storage strategies. Finally, an enhanced faceted browsing system is developed to support users' exploration of large multi-tagged document collections. It provides summary measures of document result sets at each step of navigation through a set of indicative terms and diverse set of documents, as well as information scent that helps to guide users' exploration. These summaries are derived from pre-materialized views that allow for quick calculation of centroids for various result sets. The utility and efficiency of the system is demonstrated on the New York Times Annotated Corpus

User-centered semantic dataset retrieval

Finding relevant research data is an increasingly important but time-consuming task in daily research practice. Several studies report on difficulties in dataset search, e.g., scholars retrieve only partial pertinent data, and important information can not be displayed in the user interface. Overcoming these problems has motivated a number of research efforts in computer science, such as text mining and semantic search. In particular, the emergence of the Semantic Web opens a variety of novel research perspectives. Motivated by these challenges, the overall aim of this work is to analyze the current obstacles in dataset search and to propose and develop a novel semantic dataset search. The studied domain is biodiversity research, a domain that explores the diversity of life, habitats and ecosystems. This thesis has three main contributions: (1) We evaluate the current situation in dataset search in a user study, and we compare a semantic search with a classical keyword search to explore the suitability of semantic web technologies for dataset search. (2) We generate a question corpus and develop an information model to figure out on what scientific topics scholars in biodiversity research are interested in. Moreover, we also analyze the gap between current metadata and scholarly search interests, and we explore whether metadata and user interests match. (3) We propose and develop an improved dataset search based on three components: (A) a text mining pipeline, enriching metadata and queries with semantic categories and URIs, (B) a retrieval component with a semantic index over categories and URIs and (C) a user interface that enables a search within categories and a search including further hierarchical relations. Following user centered design principles, we ensure user involvement in various user studies during the development process

Supporting the sensemaking process in visual analytics

Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. It involves interactive exploration of data using visualizations and automated data analysis to gain insight, and to ultimately make better decisions. It aims to support the sensemaking process in which information is collected, organized and analyzed to form new knowledge and inform further action. Interactive visual exploration of the data can lead to many discoveries in terms of relations, patterns, outliers and so on. It is difficult for the human working memory to keep track of all findings during a visual analysis. Also, synthesis of many different findings and relations between those findings increase the information overload and thereby hinders the sensemaking process further. The central theme of this dissertation is How to support users in their sensemaking process during interactive exploration of data? To support the sensemaking process in visual analytics, we mainly focus on how to support users to capture, reuse, review, share, and present the key aspects of interest concerning the analysis process and the findings during interactive exploration of data. For this, we have developed generic models and tools that enable users to capture findings with provenance, and construct arguments; and to review, revise and share their visual analysis. First, we present a sensemaking framework for visual analytics that contains three linked views: a data view, a navigation view and a knowledge view for supporting the sense-making process. The data view offers interactive data visualization tools. The navigation view automatically captures the interaction history using a semantically rich action model and provides an overview of the analysis structure. The knowledge view is a basic graphics editor that helps users to record findings with provenance and to organize findings into claims using diagramming techniques. Users can exploit automatically captured interaction history and manually recorded findings to review and revise their visual analysis. Thus, the analysis process can be archived and shared with others for collaborative visual analysis. Secondly, we enable analysts to capture data selections as semantic zones during an analysis, and to reuse these zones on different subsets of data. We present a Select & Slice table that helps analysts to capture, manipulate, and reuse these zones more explicitly during exploratory data analysis. Users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data slices. Finally, exploration overviews and searching techniques based on keywords, content similarity, and context helped analysts to develop awareness over the key aspects of the exploration concerning the analysis process and findings. On one hand, they can proactively search analysis processes and findings for reviewing purposes. On the other hand, they can use the system to discover implicit connections between findings and the current line of inquiry, and recommend these related findings during an interactive data exploration. We implemented the models and tools described in this dissertation in Aruvi and HARVEST. Using Aruvi and HARVEST, we studied the implications of these models on a user’s sensemaking process. We adopted the short-term and long-term case studies approach to study support offered by these tools for the sensemaking process. The observations of the case studies were used to evaluate the models

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Query-Time Data Integration

Today, data is collected in ever increasing scale and variety, opening up enormous potential for new insights and data-centric products. However, in many cases the volume and heterogeneity of new data sources precludes up-front integration using traditional ETL processes and data warehouses. In some cases, it is even unclear if and in what context the collected data will be utilized. Therefore, there is a need for agile methods that defer the effort of integration until the usage context is established. This thesis introduces Query-Time Data Integration as an alternative concept to traditional up-front integration. It aims at enabling users to issue ad-hoc queries on their own data as if all potential other data sources were already integrated, without declaring specific sources and mappings to use. Automated data search and integration methods are then coupled directly with query processing on the available data. The ambiguity and uncertainty introduced through fully automated retrieval and mapping methods is compensated by answering those queries with ranked lists of alternative results. Each result is then based on different data sources or query interpretations, allowing users to pick the result most suitable to their information need. To this end, this thesis makes three main contributions. Firstly, we introduce a novel method for Top-k Entity Augmentation, which is able to construct a top-k list of consistent integration results from a large corpus of heterogeneous data sources. It improves on the state-of-the-art by producing a set of individually consistent, but mutually diverse, set of alternative solutions, while minimizing the number of data sources used. Secondly, based on this novel augmentation method, we introduce the DrillBeyond system, which is able to process Open World SQL queries, i.e., queries referencing arbitrary attributes not defined in the queried database. The original database is then augmented at query time with Web data sources providing those attributes. Its hybrid augmentation/relational query processing enables the use of ad-hoc data search and integration in data analysis queries, and improves both performance and quality when compared to using separate systems for the two tasks. Finally, we studied the management of large-scale dataset corpora such as data lakes or Open Data platforms, which are used as data sources for our augmentation methods. We introduce Publish-time Data Integration as a new technique for data curation systems managing such corpora, which aims at improving the individual reusability of datasets without requiring up-front global integration. This is achieved by automatically generating metadata and format recommendations, allowing publishers to enhance their datasets with minimal effort. Collectively, these three contributions are the foundation of a Query-time Data Integration architecture, that enables ad-hoc data search and integration queries over large heterogeneous dataset collections