Toward personalized pan-omic association analysis under complex structures and big data

Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequent Pattern growth (MR-PFP) algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives (HAR), CombineFileInputFormat (CFIF), and Sequence Files (SF), to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth (FP-growth) algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth (PFP) algorithm in efficiency and scalability.

Download Full-text

Data Analysis Method of Intelligent Analysis Platform for Big Data of Film and Television

Complexity ◽

10.1155/2021/9947832 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Youwen Ma ◽

Yi Wan

Keyword(s):

Cloud Computing ◽

Big Data ◽

Association Analysis ◽

Programming Model ◽

Analysis Method ◽

Television Industry ◽

Film And Television ◽

Box Office ◽

The Impact ◽

Audience Rating

Based on cloud computing and statistics theory, this paper proposes a reasonable analysis method for big data of film and television. The method selects Hadoop open source cloud platform as the basis, combines the MapReduce distributed programming model and HDFS distributed file storage system and other key cloud computing technologies. In order to cope with different data processing needs of film and television industry, association analysis, cluster analysis, factor analysis, and K-mean + association analysis algorithm training model were applied to model, process, and analyze the full data of film and TV series. According to the film type, producer, production region, investment, box office, audience rating, network score, audience group, and other factors, the film and television data in recent years are analyzed and studied. Based on the study of the impact of each attribute of film and television drama on film box office and TV audience rating, it is committed to the prediction of film and television industry and constantly verifies and improves the algorithm model.

Download Full-text

The big data of violent events: algorithms for association analysis using spatio-temporal storytelling

GeoInformatica ◽

10.1007/s10707-016-0247-0 ◽

2016 ◽

Vol 20 (4) ◽

pp. 879-921 ◽

Cited By ~ 3

Author(s):

Raimundo F. Dos Santos ◽

Arnold Boedihardjo ◽

Sumit Shah ◽

Feng Chen ◽

Chang-Tien Lu ◽

...

Keyword(s):

Big Data ◽

Association Analysis ◽

Spatio Temporal ◽

Violent Events

Download Full-text

Cell morphology, volume, and surface area determinations from multilevel contouring within HVEM micrographs of serial sections and whole-cell mounts

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100127426 ◽

1987 ◽

Vol 45 ◽

pp. 588-589

Author(s):

M. Marko ◽

A. Leith ◽

D. Parsons

Keyword(s):

Surface Area ◽

Electron Micrographs ◽

Cell Morphology ◽

Individual Variation ◽

Serial Section ◽

Stereo Pair ◽

Complex Structures ◽

Serial Sections ◽

Surface Areas ◽

Computer Based

The use of serial sections and computer-based 3-D reconstruction techniques affords an opportunity not only to visualize the shape and distribution of the structures being studied, but also to determine their volumes and surface areas. Up until now, this has been done using serial ultrathin sections.The serial-section approach differs from the stereo logical methods of Weibel in that it is based on the Information from a set of single, complete cells (or organelles) rather than on a random 2-dimensional sampling of a population of cells. Because of this, it can more easily provide absolute values of volume and surface area, especially for highly-complex structures. It also allows study of individual variation among the cells, and study of structures which occur only infrequently.We have developed a system for 3-D reconstruction of objects from stereo-pair electron micrographs of thick specimens.

Download Full-text

Stereo modeling of HVEM specimens by serial tilting and computer graphics

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100141913 ◽

1986 ◽

Vol 44 ◽

pp. 34-35

Author(s):

J.R. McIntosh ◽

D.L. Stemple ◽

William Bishop ◽

G.W. Hannaway

Keyword(s):

Computer Graphics ◽

Analytical Methods ◽

Photographic Film ◽

Complex Structures ◽

Multiple Objects ◽

Multiple Images ◽

3 Dimensional

EM specimens often contain 3-dimensional information that is lost during micrography on a single photographic film. Two images of one specimen at appropriate orientations give a stereo view, but complex structures composed of multiple objects of graded density that superimpose in each projection are often difficult to decipher in stereo. Several analytical methods for 3-D reconstruction from multiple images of a serially tilted specimen are available, but they are all time-consuming and computationally intense.

Download Full-text

EXELFS Studies of Carbon Fibers

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100133953 ◽

1990 ◽

Vol 48 (2) ◽

pp. 72-73

Author(s):

V. Serin ◽

K. Hssein ◽

G. Zanchi ◽

J. Sévely

Keyword(s):

Carbon Fibers ◽

Energy Analysis ◽

Electron Excitation ◽

Complex Structures ◽

High Modulus ◽

Promising Technique ◽

X Ray ◽

Fine Structures ◽

X Ray Absorption

The present developments of electron energy analysis in the microscopes by E.E.L.S. allow an accurate recording of the spectra and of their different complex structures associated with the inner shell electron excitation by the incident electrons (1). Among these structures, the Extended Energy Loss Fine Structures (EXELFS) are of particular interest. They are equivalent to the well known EXAFS oscillations in X-ray absorption spectroscopy. Due to the EELS characteristic, the Fourier analysis of EXELFS oscillations appears as a promising technique for the characterization of composite materials, the major constituents of which are low Z elements. Using EXELFS, we have developed a microstructural study of carbon fibers. This analysis concerns the carbon K edge, which appears in the spectra at 285 eV. The purpose of the paper is to compare the local short range order, determined by this way in the case of Courtauld HTS and P100 ex-polyacrylonitrile carbon fibers, which are high tensile strength (HTS) and high modulus (HM) fibers respectively.

Download Full-text