An evaluation of space time cube representation of spatiotemporal patterns.

Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a data set to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap, we report on a between-subjects experiment comparing novice users' error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions, the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the data set, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users analyzing complex spatiotemporal patterns.

The Trade-offs with Space Time Cube Representation of Spatiotemporal Patterns

Space time cube representation is an information visualization technique where spatiotemporal data points are mapped into a cube. Fast and correct analysis of such information is important in for instance geospatial and social visualization applications. Information visualization researchers have previously argued that space time cube representation is beneficial in revealing complex spatiotemporal patterns in a dataset to users. The argument is based on the fact that both time and spatial information are displayed simultaneously to users, an effect difficult to achieve in other representations. However, to our knowledge the actual usefulness of space time cube representation in conveying complex spatiotemporal patterns to users has not been empirically validated. To fill this gap we report on a between-subjects experiment comparing novice users error rates and response times when answering a set of questions using either space time cube or a baseline 2D representation. For some simple questions the error rates were lower when using the baseline representation. For complex questions where the participants needed an overall understanding of the spatiotemporal structure of the dataset, the space time cube representation resulted in on average twice as fast response times with no difference in error rates compared to the baseline. These results provide an empirical foundation for the hypothesis that space time cube representation benefits users when analyzing complex spatiotemporal patterns.

Comparison of K-ary N-cube and de Bruijn Overlays in QoS-Constrained Multicast Applications

Research on the construction of logical overlay networks has gained significance in recent times. This is partly due to work on peer-to-peer (P2P) systems for locating and retrieving distributed data objects, and also scalable content distribution using end-system multicast techniques. However, there are emerging applications that require the real-time transport of data from various sources to potentially many thousands of subscribers, each having their own quality-of-service (QoS) constraints. This paper primarily focuses on the properties of two popular topologies found in interconnection networks, namely k-ary n-cubes and de Bruijn graphs. The regular structure of these graph topologies makes them easier to analyze and determine possible routes for real-time data than complete or irregular graphs. We show how these overlay topologies compare in their ability to deliver data according to the QoS constraints of many subscribers, each receiving data from specific publishing hosts. Comparisons are drawn on the ability of each topology to route data in the presence of dynamic system effects, due to end-hosts joining and departing the system. Finally, experimental results show the service guarantees and physical link stress resulting from efficient multicast trees constructed over both kinds of overlay networks.

Interpreting pedestrian behaviour by visualising and clustering movement data

Recent technological advances have increased the quantity of movement data being recorded. While valuable knowledge can be gained by analysing such data, its sheer volume creates challenges. Geovisual analytics, which helps the human cognition process by using tools to reason about data, offers powerful techniques to resolve these challenges. This paper introduces such a geovisual analytics environment for exploring movement trajectories, which provides visualisation interfaces, based on the classic space-time cube. Additionally, a new approach, using the mathematical description of motion within a space-time cube, is used to determine the similarity of trajectories and forms the basis for clustering them. These techniques were used to analyse pedestrian movement. The results reveal interesting and useful spatiotemporal patterns and clusters of pedestrians exhibiting similar behaviour.

A comparison-based diagnosis algorithm tailored for crossed cube multiprocessor systems

Comparison-based diagnosis is an effective approach to system-level fault diagnosis. Under the Maeng-Malek comparison model (NM* model), Sengupta and Dahbura proposed an O(N-5) diagnosis algorithm for general diagnosable systems with N nodes. Thanks to lower diameter and better graph embedding capability as compared with a hypercube of the same size, the crossed cube has been a promising candidate for interconnection networks. In this paper, we propose a fault diagnosis algorithm tailored for crossed cube connected multicomputer systems under the MM* model. By introducing appropriate data structures, this algorithm runs in O(Nlog(2)(2) N) time, which is linear in the size of the input. As a result, this algorithm is significantly superior to the Sengupta-Dahbura's algorithm when applied to crossed cube systems. (C) 2004 Elsevier B.V. All rights reserved.

Structural refinement and photoluminescence properties of irregular cube-like (Ca-1_Cu-x(x))TiO3 microcrystals synthesized by the microwave-hydrothermal method

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Geochemical data and magnetic measurements of IODP Expedition 308, Hole U1319A from the Gulf of Mexico

A 160 m mostly turbiditic late Pleistocene sediment sequence (IODP Expedition 308, Hole U1319A) from the Brazos-Trinity intraslope basin system off Texas was investigated with paleo- and rock magnetic methods. Numerous layers depleted in iron oxides and enriched by the ferrimagnetic iron-sulfide mineral greigite (Fe3S4) were detected by diagnostic magnetic properties. From the distribution of these layers, their stratigraphic context and the present geochemical zonation, we develop two conceptual reaction models of greigite formation in non-steady depositional environments. The "sulfidization model" predicts single or twin greigite layers by incomplete transformation of iron monosulfides with polysulfides around the sulfate methane transition (SMT). The "oxidation model" explains greigite formation by partial oxidation of iron monosulfides near the iron redox boundary during periods of downward shifting oxidation fronts. The stratigraphic record provides evidence that both these greigite formation processes act here at typical depths of about 12-14 mbsf and 3-4 mbsf. Numerous "fossil" greigite layers most likely preserved by rapid upward shifts of the redox zonation denote past SMT and sea floor positions characterized by stagnant hemipelagic sedimentation conditions. Six diagenetic stages from a pristine magnetite-dominated to a fully greigite-dominated magnetic mineralogy were differentiated by combination of various hysteresis and remanence parameters.

New morpho-bathymetric and tectono-stratigraphic data on Naples and Salerno gulfs (southern tyrrhenian sea, Italy) derived from bathymetric and seismic data analysis and integrated geological interpretation

New morpho-bathymetric and tectono-stratigraphic data on Naples and Salerno Gulfs, derived from bathymetric and seismic data analysis and integrated geologic interpretation are here presented. The CUBE(Combined Uncertainty Bathymetric Estimator) method has been applied to complex morphologies, such as the Capri continental slope and the related geological structures occurring in the Salerno Gulf.The bathymetric data analysis has been carried out for marine geological maps of the whole Campania continental margin at scales ranging from 1:25.000 to 1:10.000, including focused examples in Naples and Salerno Gulfs, Naples harbour, Capri and Ischia Islands and Salerno Valley. Seismic data analysis has allowed for the correlation of main morpho-structural lineaments recognized at a regional scale through multichannel profiles with morphological features cropping out at the sea bottom, evident from bathymetry.Main fault systems in the area have been represented on a tectonic sketch map, including the master fault located northwards to the Salerno Valley half graben. Some normal faults parallel to the master fault have been interpreted from the slope map derived from bathymetric data. A complex system of antithetic faults bound two morpho-structural highs located 20km to the south of the Capri Island. Some hints of compressional reactivation of normal faults in an extensional setting involving the whole Campania continental margin have been shown from seismic interpretation.

Discovery driven analysis on semi-structured text data

Discovery Driven Analysis (DDA) is a common feature of OLAP technology to analyze structured data. In essence, DDA helps analysts to discover anomalous data by highlighting 'unexpected' values in the OLAP cube. By giving indications to the analyst on what dimensions to explore, DDA speeds up the process of discovering anomalies and their causes. However, Discovery Driven Analysis (and OLAP in general) is only applicable on structured data, such as records in databases. We propose a system to extend DDA technology to semi-structured text documents, that is, text documents with a few structured data. Our system pipeline consists of two stages: first, the text part of each document is structured around user specified dimensions, using semi-PLSA algorithm; then, we adapt DDA to these fully structured documents, thus enabling DDA on text documents. We present some applications of this system in OLAP analysis and show how scalability issues are solved. Results show that our system can handle reasonable datasets of documents, in real time, without any need for pre-computation.