Most tabular data visualization techniques focus on overviews, yet many practical analysis tasks are concerned with investigating individual items of interest. At the same time, relating an item to the rest of a potentially large table is important. In this work we present Taggle, a tabular visualization technique for exploring and presenting large and complex tables. Taggle takes an item-centric, spreadsheet-like approach, visualizing each row in the source data individually using visual encodings for the cells. At the same time, Taggle introduces data-driven aggregation of data subsets. The aggregation strategy is complemented by interaction methods tailored to answer specific analysis questions, such as sorting based on multiple columns and rich data selection and filtering capabilities. We demonstrate Taggle using a case study conducted by a domain expert on complex genomics data analysis for the purpose of drug discovery.
Domino @cite_36 is a hybrid tabular overview technique. It is based on the concept of placing subsets of a dataset on a canvas and choosing a suitable representation for it ). Multiple subsets can then be connected to show their relationships in various ways. @cite_26 , VisBricks @cite_13 and StratomeX @cite_21 @cite_42 are related hybrid techniques, but are more restricted with respect to the selection of subsets.
@cite_26 @cite_36 @cite_21 @cite_42 @cite_13
2070967917 2409822643 1966915805 2178966606
Answering questions about complex issues often requires analysts to take into account information contained in multiple interconnected datasets. A common strategy in analyzing and visualizing large and heterogeneous data is dividing it into meaningful subsets. Interesting subsets can then be selected and the associated data and the relationships between the subsets visualized. However, neither the extraction and manipulation nor the comparison of subsets is well supported by state-of-the-art techniques. In this paper we present Domino, a novel multiform visualization technique for effectively representing subsets and the relationships between them. By providing comprehensive tools to arrange, combine, and extract subsets, Domino allows users to create both common visualization techniques and advanced visualizations tailored to specific use cases. In addition to the novel technique, we present an implementation that enables analysts to manage the wide range of options that our approach offers. Innovative interactive features such as placeholders and live previews support rapid creation of complex analysis setups. We introduce the technique and the implementation using a simple example and demonstrate scalability and effectiveness in a use case from the field of cancer genomics. Local binary pattern (LBP) is widely adopted for efficient image feature description and simplicity. To describe the color images, it is required to combine the LBPs from each channel of the image. The traditional way of binary combination is to simply concatenate the LBPs from each channel, but it increases the dimensionality of the pattern. In order to cope with this problem, this paper proposes a novel method for image description with multichannel decoded LBPs. We introduce adder- and decoder-based two schemas for the combination of the LBPs from more than one channel. Image retrieval experiments are performed to observe the effectiveness of the proposed approaches and compared with the existing ways of multichannel techniques. The experiments are performed over 12 benchmark natural scene and color texture image databases, such as Corel-1k, MIT-VisTex, USPTex, Colored Brodatz, and so on. It is observed that the introduced multichannel adder- and decoder-based LBPs significantly improve the retrieval performance over each database and outperform the other multichannel-based approaches in terms of the average retrieval precision and average retrieval rate. We present SchemEX, an approach and tool for a stream-based indexing and schema extraction of Linked Open Data (LOD) at web-scale. The schema index provided by SchemEX can be used to locate distributed data sources in the LOD cloud. It serves typical LOD information needs such as finding sources that contain instances of one specific data type, of a given set of data types (so-called type clusters), or of instances in type clusters that are connected by one or more common properties (so-called equivalence classes). The entire process of extracting the schema from triples and constructing an index is designed to have linear runtime complexity. Thus, the schema index can be computed on-the-fly while the triples are crawled and provided as a stream by a linked data spider. To demonstrate the web-scalability of our approach, we have computed a SchemEX index over the Billion Triples Challenge (BTC) dataset 2011 consisting of 2,170 million triples. In addition, we have computed the SchemEX index on a dataset with 11 million triples. We use this smaller dataset for conducting a detailed qualitative analysis. We are capable of locating relevant data sources with recall between 71 and 98 and a precision between 74 and 100 at a window size of 100 K triples observed in the stream and depending on the complexity of the query, i.e. if one wants to find specific data types, type clusters or equivalence classes. Abstract A simple, fully automated, and efficient method to determine the structural properties and evolution (tracking) of cloud shields of convective systems (CS) is described. The method, which is based on the maximum spatial correlation tracking technique (MASCOTTE), is a new alternative to the existent techniques available for studies that monitor the evolution of CS using satellite images. MASCOTTE provides as CS structural properties the following parameters: mean and variance of brightness temperature, horizontal area, perimeter, minimum brightness temperature, fractional convective area, center of gravity, and fragmentation. The fragmentation parameter has the potential to monitor the evolution of the CS. A new way of estimating the orientation and eccentricity of CS is proposed and is based on the empirical orthogonal function analysis of CS pixel coordinates. The method includes an accurate detection of splitting and merging of convective systems, which is a critical step in the automated satel...