Visual exploration of flow fields is important for studying dynamic systems. We introduce semantic flow graph (SFG), a novel graph representation and interaction framework that enables users to explore the relationships among key objects (i.e., field lines, features, and spatiotemporal regions) of both steady and unsteady flow fields. The objects and their relationships are organized as a heter...

We introduce semantic flow graph (SFG), a novel graph representation and interaction framework that enables users to explore the relationships among key objects (i.e., streamlines, critical points, and spatial regions) of a 3D flow field. The objects and their relationships are organized as a heterogeneous network. We assign each object a set of attributes, based on which a semantic abstraction...

syntax tree Tree representation of a computer program, that keeps only the semantically significant information and abstracts away syntactic sugar such as indentation, parentheses, or separators. control flow graph Representation of a procedure body as a directed graph, whose nodes, known as basic blocks, contain each a list of instructions to be executed in sequence, and whose arcs represent a...

Test sequence generation through code is mainly done by using some sort of a flow graph viz. Control Flow Graph (CFG), Concurrent Control Flow Graph (CCFG), event Graph etc. approaches that use UmL also need flow graph as an intermediate representation for final test sequence generation. In the present approach, a Flow Graph for a new concept i.e. Java7 Fork/Join is constructed and hence, by tr...

spectral-based subspace learning is a common data preprocessing step in many machine pipelines. The main aim to learn meaningful low dimensional embedding of the data. However, most methods do not take into consideration possible measurement inaccuracies or artifacts that can lead with high uncertainty. Thus, directly from raw be misleading and negatively impact accuracy. In this paper, we prop...

In this work, we extend the formulation of spatial-based graph convolutional networks with a new architecture, called graph-informed neural network (GINN). This architecture is specifically designed for regression tasks on graph-structured data that are not suitable well-known networks, such as functions domain and codomain defined two sets values vertices graph. particular, formulate (GI) laye...

Faculty of Electrical Engineering, Mathematics and Computer Science CE-MS-2012-05 A streaming application like software defined radio (SDR) executed on a heterogeneous multi-processor system on chip (MPSoC) consists of various transceiver jobs that have to be scheduled on different processors concurrently. The hard real time performance requirements of these jobs can be guaranteed on the multi-...

We propose a number of techniques for obtaining a global ranking from data that may be incomplete and imbalanced — characteristics that are almost universal to modern datasets coming from e-commerce and internet applications. We are primarily interested in cardinal data based on scores or ratings though our methods also give specific insights on ordinal data. From raw ranking data, we construct...

While there have been intensive efforts in developing better 3D flow visualization techniques, little attention has been paid to the design of better user interfaces and more effective data exploration work flow. In this paper, we propose a novel graph-based user interface called Flow Web to enable more systematic explorations of 3D flow data. The Flow Web is a node-link graph that is construct...

Modern data analysis applications, require the ability to provide on-demand integration of sources while offering a flexible and user-friendly query interface. Traditional techniques for answering queries using views, focused on rather static setting, fail address such requirements. To overcome these issues, we propose fully-fledged approach based graph-based constructs. The extensibility graph...