TY - CONF
T1 - Component-based Data Layout for Efficient Slicing of Very Large Multidimensional Volumetric Data
T2 - Scientific and Statistical Database Management, 2007. SSBDM '07. 19th International Conference on
Y1 - 2007
A1 - Kim,Jusub
A1 - JaJa, Joseph F.
KW - axis-aligned
KW - cache
KW - curves;very
KW - data
KW - data;data
KW - databases;
KW - handling;query
KW - large
KW - layout;data
KW - memory
KW - multidimensional
KW - processing;very
KW - queries;space-filling
KW - size;component-based
KW - slicing
KW - slicing;out-of-core
KW - volumetric
AB - In this paper, we introduce a new efficient data layout scheme to efficiently handle out-of-core axis-aligned slicing queries of very large multidimensional volumetric data. Slicing is a very useful dimension reduction tool that removes or reduces occlusion problems in visualizing 3D/4D volumetric data sets and that enables fast visual exploration of such data sets. We show that the data layouts based on typical space-filling curves are not optimal for the out-of-core slicing queries and present a novel component-based data layout scheme for a specialized problem domain, in which it is only required to provide fast slicing at every k-th value, for any k gt; 1. Our component-based data layout scheme provides much faster processing time for any axis-aligned slicing direction at every k-th value, k gt; 1, requiring less cache memory size and without any replication of data. In addition, the data layout can be generalized to any high dimension.
JA - Scientific and Statistical Database Management, 2007. SSBDM '07. 19th International Conference on
M3 - 10.1109/SSDBM.2007.7
ER -
TY - CONF
T1 - Illuminating light field: image-based face recognition across illuminations and poses
T2 - Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on
Y1 - 2004
A1 - Zhou,Shaohua
A1 - Chellapa, Rama
KW - Face
KW - field;
KW - illuminating
KW - image-based
KW - Lambertain
KW - light
KW - lighting;
KW - model;
KW - multidimensional
KW - poses;
KW - processing;
KW - recognition;
KW - reflectance
KW - reflectivity;
KW - signal
AB - We present an image-based method for face recognition across different illuminations and different poses, where the term 'image-based' means that only 2D images are used and no explicit 3D models are needed. As face recognition across illuminations and poses involves three factors, namely identity, illumination, and pose, generalizations from known identities to novel identities, from known illuminations to novel illuminations, and from known poses to unknown poses are desired. Our approach, called the illuminating light field, derives an identity signature that is invariant to illuminations and poses, where a subspace encoding is assumed for the identity, a Lambertain reflectance model for the illumination, and a light field model for the poses. Experimental results using the PIE database demonstrate the effectiveness of the proposed approach.
JA - Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on
M3 - 10.1109/AFGR.2004.1301536
ER -
TY - CONF
T1 - Strategies for exploring large scale data
T2 - Parallel Architectures, Algorithms and Networks, 2004. Proceedings. 7th International Symposium on
Y1 - 2004
A1 - JaJa, Joseph F.
KW - algorithms;
KW - association;
KW - asymptotic
KW - bounds;
KW - business
KW - data
KW - data;
KW - database
KW - databases;
KW - demographic
KW - discovery;
KW - Indexing
KW - indexing;
KW - information
KW - knowledge
KW - large
KW - linear
KW - mining;
KW - multidimensional
KW - objects;
KW - optimal
KW - Parallel
KW - pattern
KW - processing;
KW - query
KW - querying;
KW - range
KW - scale
KW - scientific
KW - search
KW - serial
KW - series
KW - series;
KW - simulation
KW - space
KW - structure;
KW - structures;
KW - techniques;
KW - temporal
KW - TIME
KW - value
KW - very
KW - window;
AB - We consider the problem of querying large scale multidimensional time series data to discover events of interest, test and validate hypotheses, or to associate temporal patterns with specific events. This type of data currently dominates most other types of available data, and will very likely become even more prevalent in the future given the current trends in collecting time series of business, scientific, demographic, and simulation data. The ability to explore such collections interactively, even at a coarse level, will be critical in discovering the information and knowledge embedded in such collections. We develop indexing techniques and search algorithms to efficiently handle temporal range value querying of multidimensional time series data. Our indexing uses linear space data structures that enable the handling of queries in I/O time that is essentially the same as that of handling a single time slice, assuming the availability of a logarithmic number of processors as a function of the temporal window. A data structure with provably almost optimal asymptotic bounds is also presented for the case when the number of multidimensional objects is relatively small. These techniques improve significantly over standard techniques for either serial or parallel processing, and are evaluated by extensive experimental results that confirm their superior performance.
JA - Parallel Architectures, Algorithms and Networks, 2004. Proceedings. 7th International Symposium on
M3 - 10.1109/ISPAN.2004.1300447
ER -
TY - CONF
T1 - A Smale-like decomposition for discrete scalar fields
T2 - Pattern Recognition, 2002. Proceedings. 16th International Conference on
Y1 - 2002
A1 - De Floriani, Leila
A1 - Mesmoudi,M. M.
A1 - Danovaro,E.
KW - data
KW - decomposition;
KW - differentiable
KW - discrete
KW - domain;
KW - field;
KW - fields;
KW - functions;
KW - gradient
KW - graph-based
KW - methods;
KW - multidimensional
KW - multiresolution
KW - representation;
KW - scalar
KW - Smale-like
KW - structure
KW - Topology
KW - triangulated
KW - vector
KW - visualisation;
AB - In this paper we address the problem of representing the structure of the topology of a d-dimensional scalar field as a basis for constructing a multiresolution representation of the structure of such afield. To this aim, we define a discrete decomposition of a triangulated d-dimensional domain, on whose vertices the values of the field are given. We extend a Smale decomposition, defined by Thom (1949) and Smale (1960) for differentiable functions, to the discrete case, to what we call a Smale-like decomposition. We introduce the notion of discrete gradient vector field, which indicates the growth of the scalar field and matches with our decomposition. We sketch an algorithm for building a Smale-like decomposition and a graph-based representation of this decomposition. We present results for the case of two-dimensional fields.
JA - Pattern Recognition, 2002. Proceedings. 16th International Conference on
VL - 1
M3 - 10.1109/ICPR.2002.1044644
ER -
TY - JOUR
T1 - VLSI architectures for multidimensional transforms
JF - Computers, IEEE Transactions on
Y1 - 1991
A1 - Chakrabarti,C.
A1 - JaJa, Joseph F.
KW - architecture;
KW - architectures;
KW - arithmetic;
KW - complexity;
KW - computational
KW - Computer
KW - digital
KW - fixed-precision
KW - linear
KW - multidimensional
KW - separable
KW - transforms;
KW - VLSI
AB - The authors propose a family of VLSI architectures with area-time tradeoffs for computing (N times;N times; . . . times;N) d-dimensional linear separable transforms. For fixed-precision arithmetic with b bits, the architectures have an area A=O(N^{d+2a}) and computation time T=O(dN^{d/2-a}b ), and achieve the AT^{2} bound of AT^{2}=O(n^{2}b ^{2}) for constant d, where n=N^{d } and O lt;a les;d/2
VL - 40
SN - 0018-9340
CP - 9
M3 - 10.1109/12.83648
ER -