26 research outputs found
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
New Fundamental Technologies in Data Mining
The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining
Engineering and Scientific Data Management
The application of data management systems to engineering and scientific data is described
Combining SOA and BPM Technologies for Cross-System Process Automation
This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation
Proactive-reactive, robust scheduling and capacity planning of deconstruction projects under uncertainty
A project planning and decision support model is developed and applied to identify and reduce risk and uncertainty in deconstruction project planning. It allows calculating building inventories based on sensor information and construction standards and it computes robust project plans for different scenarios with multiple modes, constrained renewable resources and locations. A reactive and flexible planning element is proposed in the case of schedule infeasibility during project execution
Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning
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228326pre.pdf (preprint version ) (Open Access)
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228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202
Exploratory search in time-oriented primary data
In a variety of research fields, primary data that describes scientific phenomena in an original condition is obtained.
Time-oriented primary data, in particular, is an indispensable data type, derived from complex measurements depending
on time. Today, time-oriented primary data is collected at rates that exceed the domain experts’ abilities to seek
valuable information undiscovered in the data. It is widely accepted that the magnitudes of uninvestigated data will
disclose tremendous knowledge in data-driven research, provided that domain experts are able to gain insight into the
data. Domain experts involved in data-driven research urgently require analytical capabilities. In scientific practice,
predominant activities are the generation and validation of hypotheses. In analytical terms, these activities are often
expressed in confirmatory and exploratory data analysis. Ideally, analytical support would combine the strengths of
both types of activities.
Exploratory search (ES) is a concept that seamlessly includes information-seeking behaviors ranging from search
to exploration. ES supports domain experts in both gaining an understanding of huge and potentially unknown data
collections and the drill-down to relevant subsets, e.g., to validate hypotheses. As such, ES combines predominant tasks
of domain experts applied to data-driven research. For the design of useful and usable ES systems (ESS), data scientists
have to incorporate different sources of knowledge and technology. Of particular importance is the state-of-the-art
in interactive data visualization and data analysis. Research in these factors is at heart of Information Visualization
(IV) and Visual Analytics (VA). Approaches in IV and VA provide meaningful visualization and interaction designs,
allowing domain experts to perform the information-seeking process in an effective and efficient way. Today, bestpractice
ESS almost exclusively exist for textual data content, e.g., put into practice in digital libraries to facilitate the
reuse of digital documents. For time-oriented primary data, ES mainly remains at a theoretical state.
Motivation and Problem Statement. This thesis is motivated by two main assumptions. First, we expect that
ES will have a tremendous impact on data-driven research for many research fields. In this thesis, we focus on
time-oriented primary data, as a complex and important data type for data-driven research. Second, we assume that
research conducted to IV and VA will particularly facilitate ES. For time-oriented primary data, however, novel
concepts and techniques are required that enhance the design and the application of ESS. In particular, we observe a
lack of methodological research in ESS for time-oriented primary data. In addition, the size, the complexity, and the
quality of time-oriented primary data hampers the content-based access, as well as the design of visual interfaces
for gaining an overview of the data content. Furthermore, the question arises how ESS can incorporate techniques
for seeking relations between data content and metadata to foster data-driven research. Overarching challenges for
data scientists are to create usable and useful designs, urgently requiring the involvement of the targeted user group
and support techniques for choosing meaningful algorithmic models and model parameters. Throughout this thesis,
we will resolve these challenges from conceptual, technical, and systemic perspectives. In turn, domain experts can
benefit from novel ESS as a powerful analytical support to conduct data-driven research.
Concepts for Exploratory Search Systems (Chapter 3). We postulate concepts for the ES in time-oriented primary
data. Based on a survey of analysis tasks supported in IV and VA research, we present a comprehensive selection of
tasks and techniques relevant for search and exploration activities. The assembly guides data scientists in the choice of
meaningful techniques presented in IV and VA. Furthermore, we present a reference workflow for the design and
the application of ESS for time-oriented primary data. The workflow divides the data processing and transformation
process into four steps, and thus divides the complexity of the design space into manageable parts. In addition, the
reference workflow describes how users can be involved in the design. The reference workflow is the framework for
the technical contributions of this thesis.
Visual-Interactive Preprocessing of Time-Oriented Primary Data (Chapter 4). We present a visual-interactive
system that enables users to construct workflows for preprocessing time-oriented primary data. In this way, we
introduce a means of providing content-based access. Based on a rich set of preprocessing routines, users can create
individual solutions for data cleansing, normalization, segmentation, and other preprocessing tasks. In addition, the
system supports the definition of time series descriptors and time series distance measures. Guidance concepts support
users in assessing the workflow generalizability, which is important for large data sets. The execution of the workflows
transforms time-oriented primary data into feature vectors, which can subsequently be used for downstream search
and exploration techniques. We demonstrate the applicability of the system in usage scenarios and case studies.
Content-Based Overviews (Chapter 5). We introduce novel guidelines and techniques for the design of contentbased
overviews. The three key factors are the creation of meaningful data aggregates, the visual mapping of these
aggregates into the visual space, and the view transformation providing layouts of these aggregates in the display
space. For each of these steps, we characterize important visualization and interaction design parameters allowing the
involvement of users. We introduce guidelines supporting data scientists in choosing meaningful solutions. In addition,
we present novel visual-interactive quality assessment techniques enhancing the choice of algorithmic model and
model parameters. Finally, we present visual interfaces enabling users to formulate visual queries of the time-oriented
data content. In this way, we provide means of combining content-based exploration with content-based search.
Relation Seeking Between Data Content and Metadata (Chapter 6). We present novel visual interfaces enabling
domain experts to seek relations between data content and metadata. These interfaces can be integrated into ESS
to bridge analytical gaps between the data content and attached metadata. In three different approaches, we focus
on different types of relations and define algorithmic support to guide users towards most interesting relations.
Furthermore, each of the three approaches comprises individual visualization and interaction designs, enabling users
to explore both the data and the relations in an efficient and effective way. We demonstrate the applicability of our
interfaces with usage scenarios, each conducted together with domain experts. The results confirm that our techniques
are beneficial for seeking relations between data content and metadata, particularly for data-centered research.
Case Studies - Exploratory Search Systems (Chapter 7). In two case studies, we put our concepts and techniques
into practice. We present two ESS constructed in design studies with real users, and real ES tasks, and real timeoriented
primary data collections. The web-based VisInfo ESS is a digital library system facilitating the visual access to
time-oriented primary data content. A content-based overview enables users to explore large collections of time series
measurements and serves as a baseline for content-based queries by example. In addition, VisInfo provides a visual
interface for querying time oriented data content by sketch. A result visualization combines different views of the data
content and metadata with faceted search functionality. The MotionExplorer ESS supports domain experts in human
motion analysis. Two content-based overviews enhance the exploration of large collections of human motion capture
data from two perspectives. MotionExplorer provides a search interface, allowing domain experts to query human
motion sequences by example. Retrieval results are depicted in a visual-interactive view enabling the exploration of
variations of human motions. Field study evaluations performed for both ESS confirm the applicability of the systems
in the environment of the involved user groups. The systems yield a significant improvement of both the effectiveness
and the efficiency in the day-to-day work of the domain experts. As such, both ESS demonstrate how large collections
of time-oriented primary data can be reused to enhance data-centered research.
In essence, our contributions cover the entire time series analysis process starting from accessing raw time-oriented
primary data, processing and transforming time series data, to visual-interactive analysis of time series. We present
visual search interfaces providing content-based access to time-oriented primary data. In a series of novel explorationsupport
techniques, we facilitate both gaining an overview of large and complex time-oriented primary data collections
and seeking relations between data content and metadata. Throughout this thesis, we introduce VA as a means of
designing effective and efficient visual-interactive systems. Our VA techniques empower data scientists to choose
appropriate models and model parameters, as well as to involve users in the design. With both principles, we support
the design of usable and useful interfaces which can be included into ESS. In this way, our contributions bridge the gap
between search systems requiring exploration support and exploratory data analysis systems requiring visual querying
capability. In the ESS presented in two case studies, we prove that our techniques and systems support data-driven
research in an efficient and effective way