Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.

Reference list of 120 datasets from time series station Payerne used for exploratory search

The analysis of time-dependent data is an important problem in many application domains, and interactive visualization of time-series data can help in understanding patterns in large time series data. Many effective approaches already exist for visual analysis of univariate time series supporting tasks such as assessment of data quality, detection of outliers, or identification of periodically or frequently occurring patterns. However, much fewer approaches exist which support multivariate time series. The existence of multiple values per time stamp makes the analysis task per se harder, and existing visualization techniques often do not scale well. We introduce an approach for visual analysis of large multivariate time-dependent data, based on the idea of projecting multivariate measurements to a 2D display, visualizing the time dimension by trajectories. We use visual data aggregation metaphors based on grouping of similar data elements to scale with multivariate time series. Aggregation procedures can either be based on statistical properties of the data or on data clustering routines. Appropriately defined user controls allow to navigate and explore the data and interactively steer the parameters of the data aggregation to enhance data analysis. We present an implementation of our approach and apply it on a comprehensive data set from the field of earth bservation, demonstrating the applicability and usefulness of our approach.

(Table 1) Organic geochemistry at DSDP Leg 71 Holes

Forty-three core samples from Sites 511 through 514 of DSDP Leg 71 were analyzed geochemically. The black shales at the bottom of Hole 511, in the basin province of the Falkland Plateau, contain an average of 1590 ppm extractable organic matter (EOM) and 120 ppm hydrocarbons. Whereas molecular type-carbon number distributions of mono- and polynuclear aromatic hydrocarbons and their sulphur and oxygen analogues in the black shale "aromatic hydrocarbon" fractions are very similar to those of many crude oils, other data - gas chromatography (GC) fingerprint, pyrolysis GC, visual kerogen analysis, H/C ratio - suggest the black shale section is thermally immature. Together, these observations imply that many of the hydrocarbons were deposited with the original sediments or are diagenetic products of other biological compounds. Pyrograms of the humic acid and kerogen fractions from the black shale interval are typical of geopolymers derived from marine algal material. It appears that these humic acids and kerogens are derived from the same lipid stock.

Accumulation rates and composition of organic matter in sediments off Peru

Deep-sea sediment samples from three Ocean Drilling Program (ODP) Leg 112 sites on the Peru continental margin were investigated, using a number of organic geochemical and organic petrographic techniques, for amounts and compositions of the organic matter preserved. Preliminary results include mass accumulation rates of organic carbon at Site 679 and characteristics of the organic facies for sediments from Sites 679, 681, and 684. Organic-carbon contents are high, with few exceptions. Particularly high values were determined in the Pliocene interval at Site 684 (4%-7.5%) and in the early Pliocene to Quaternary section of Hole 679D (2%-9%). Older sediments at this site have distinctively lower organic-carbon contents (0.2%-2.5%). Mass accumulation rates of organic matter at Site 679 are 0.02 to 0.07 g carbon/cm**2/k.y. for late Miocene to early Pliocene sediments and higher by a factor of 5 to 10 in the Quaternary sediments. The organic matter in all samples has a predominantly marine planktonic and bacterial origin, with minor terrigenous contribution. Organic particle sizes are strikingly small, so that only a minor portion is covered by visual maceral analysis. Molecular organic-geochemical data were obtained for nonaromatic hydrocarbons, aromatic hydrocarbons (including sulfur compounds), alcohols, ketones, esters, and carboxylic acids. Among the total extractable lipids, long-chain unsaturated ketones from Prymnesiophyte algae strongly predominate among the gas chromatography (GC) amenable components. Steroids are major constituents of the ketone and free- and bound-alcohol fractions. Perylene is the most abundant aromatic hydrocarbon, whereas in the nonaromatic hydrocarbon fractions, long-chain n-alkanes from higher land plants predominate, although the total terrigenous organic matter proportion in the sediments is small.