Research of Big Data Space-Time Analytics for Clouding Based Contexts-Aware IOV Applications

2014 ◽  
Author(s):  
Di Zheng ◽  
Kerong Ben ◽  
Hongliang Yuan
Keyword(s):  
Big Data ◽  
Space Time ◽  
Data Space

Procurement Analytics: Enabling Data Architectures in the Age of Big Data

2020 ◽  
Author(s):  
Michael Goul ◽  
T. S. Raghu ◽  
Ziru Li
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Research Literature ◽  
Structured Interviews ◽  
Data Space ◽  
Prescriptive Analytics ◽  
Data Architecture ◽  
The Future ◽  
High Level ◽  
Area Data

As procurement organizations increasingly move from a cost-and-efficiency emphasis to a profit-and-growth emphasis, flexible data architecture will become an integral part of a procurement analytics strategy. It is therefore imperative for procurement leaders to understand and address digitization trends in supply chains and to develop strategies to create robust data architecture and analytics strategies for the future. This chapter assesses and examines the ways companies can organize their procurement data architectures in the big data space to mitigate current limitations and to lay foundations for the discovery of new insights. It sets out to understand and define the levels of maturity in procurement organizations as they pertain to the capture, curation, exploitation, and management of procurement data. The chapter then develops a framework for articulating the value proposition of moving between maturity levels and examines what the future entails for companies with mature data architectures. In addition to surveying the practitioner and academic research literature on procurement data analytics, the chapter presents detailed and structured interviews with over fifteen procurement experts from companies around the globe. The chapter finds several important and useful strategies that have helped procurement organizations design strategic roadmaps for the development of robust data architectures. It then further identifies four archetype procurement area data architecture contexts. In addition, this chapter details exemplary high-level mature data architecture for each archetype and examines the critical assumptions underlying each one. Data architectures built for the future need a design approach that supports both descriptive and real-time, prescriptive analytics.

scholarly journals Microbiome Search Engine 2: a Platform for Taxonomic and Functional Search of Global Microbiomes on the Whole-Microbiome Level

mSystems ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Gongchao Jing ◽  
Lu Liu ◽  
Zengbin Wang ◽  
Yufeng Zhang ◽  
Li Qian ◽  
...  
Keyword(s):  
Big Data ◽  
User Interface ◽  
Search Engine ◽  
Functional Similarity ◽  
Metagenomic Data ◽  
Data Sets ◽  
Data Space ◽  
Link Type ◽  
Database Platform ◽  
Microbiome Data

ABSTRACT Metagenomic data sets from diverse environments have been growing rapidly. To ensure accessibility and reusability, tools that quickly and informatively correlate new microbiomes with existing ones are in demand. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes in the global metagenome data space based on the taxonomic or functional similarity of a whole microbiome to those in the database. MSE 2 consists of (i) a well-organized and regularly updated microbiome database that currently contains over 250,000 metagenomic shotgun and 16S rRNA gene amplicon samples associated with unified metadata collected from 798 studies, (ii) an enhanced search engine that enables real-time and fast (<0.5 s per query) searches against the entire database for best-matched microbiomes using overall taxonomic or functional profiles, and (iii) a Web-based graphical user interface for user-friendly searching, data browsing, and tutoring. MSE 2 is freely accessible via http://mse.ac.cn. For standalone searches of customized microbiome databases, the kernel of the MSE 2 search engine is provided at GitHub (https://github.com/qibebt-bioinfo/meta-storms). IMPORTANCE A search-based strategy is useful for large-scale mining of microbiome data sets, such as a bird’s-eye view of the microbiome data space and disease diagnosis via microbiome big data. Here, we introduce Microbiome Search Engine 2 (MSE 2), a microbiome database platform for searching query microbiomes against the existing microbiome data sets on the basis of their similarity in taxonomic structure or functional profile. Key improvements include database extension, data compatibility, a search engine kernel, and a user interface. The new ability to search the microbiome space via functional similarity greatly expands the scope of search-based mining of the microbiome big data.

Space-Time Analytics for Spatial Dynamics

Data Mining ◽  
2013 ◽  
pp. 2117-2131
Author(s):  
May Yuan ◽  
James Bothwell
Keyword(s):  
Big Data ◽  
Temperature Change ◽  
Spatial Dynamics ◽  
Space Time ◽  
Data Sets ◽  
Spatial Processes ◽  
Multiple Sources ◽  
Time Concepts ◽  
New Thinking ◽  
New Space

The so-called Big Data Challenge poses not only issues with massive volumes of data, but issues with the continuing data streams from multiple sources that monitor environmental processes or record social activities. Many statistics tools and data mining methods have been developed to reveal embedded patterns in large data sets. While patterns are critical to data analysis, deep insights will remain buried unless we develop means to associate spatiotemporal patterns to the dynamics of spatial processes that essentially drive the formation of patterns in the data. This chapter reviews the literature with the conceptual foundation for space-time analytics dealing with spatial processes, discusses the types of dynamics that have and have not been addressed in the literature, and identifies needs for new thinking that can systematically advance space-time analytics to reveal dynamics of spatial processes. The discussion is facilitated by an example to highlight potential means of space-time analytics in response to the Big Data Challenge. The example shows the development of new space-time concepts and tools to analyze data from two common General Circulation Models for climate change predictions. Common approaches compare temperature changes at locations from the NCAR CCSM3 and from the CNRM CM3 or animate time series of temperature layers to visualize the climate prediction. Instead, new space-time analytics methods are shown here the ability to decipher the differences in spatial dynamics of the predicted temperature change in the model outputs and apply the concepts of change and movement to reveal warming, cooling, convergence, and divergence in temperature change across the globe.

scholarly journals GENERALIZED SPACE-TIME AUTOREGRESSIVE (GS-TAR) (Studi Kasus Peramalan Harga Saham Syariah Empat Perusahaan di JII)

Factor M ◽  
2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Sulis Setiya Ningsih
Keyword(s):  
Space Time ◽  
Time Model ◽  
Data Space ◽  
Diagnostic Checking ◽  
Space Time Model

Abstrak: Data deret waktu dari beberapa lokasi yang berdekatan seringkali mempunyai hubungan yang saling bergantung. Data yang tidak hanya mempunyai keterkaitan dengan kejadian pada waktu-waktu sebelumnya, tetapi juga mempunyai keterkaitan dengan lokasi lain disebut dengan data space-time. Model Generalized Space-Time Autoregrresive (GS-TAR) adalah suatu model yang banyak digunakan untuk memodelkan dan meramalkan data deret waktu dan lokasi. Adapun tujuan dari penelitian ini yaitu untuk mengaplikasikan model GS-TAR pada studi kasus memodelkan empat perusahaan yang termasuk saham syariah Jakarta Islamic Index (JII). Penelitian ini membahas tentang langkah-langkah analisis data runtun waktu dengan model GS-TAR. Metode ini terdiri dari beberapa tahap, yaitu uji stasioneritas, identifikasi model, estimasi parameter, diagnostic checking dan peramalan. Model runtun waktu GS-TAR (1;1) dapat melakukan peramalan harga saham syariah dengan baik. Hasil peramalan empat perusahaan yang diperoleh menunjukkan bahwa data dari hasil peramalan mendekati data aktual. Kata kunci : Model GS-TAR, Peramalan, Saham Syariah, Space-Time

Nanowire-Based Sensor Electronics for Chemical and Biological Applications

The Analyst ◽  
2021 ◽  
Author(s):  
Guozhu Zhang ◽  
HAO ZENG ◽  
Jiangyang Liu ◽  
Kazuki Nagashima ◽  
Tsunaki Takahashi ◽  
...  
Keyword(s):  
Big Data ◽  
Biological Species ◽  
Space Time ◽  
Biological Applications ◽  
Sensing Applications ◽  
Fundamental Science ◽  
Detection And Recognition

Detection and recognition of chemical and biological species via sensor electronics is important not only for various sensing applications but alos fundamental science utilizing collected big data in space-time. In...

scholarly journals Data Economy 2.0: From Big Data Value to AI Value and a European Data Space

2021 ◽  
pp. 379-399
Author(s):  
Sonja Zillner ◽  
Jon Ander Gomez ◽  
Ana García Robles ◽  
Thomas Hahn ◽  
Laure Le Bars ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Point Of View ◽  
Driven Systems ◽  
Significant Growth ◽  
Data Space ◽  
European Data ◽  
Data Value ◽  
Data Volume

AbstractArtificial intelligence (AI) has a tremendous potential to benefit European citizens, economy, environment and society and already demonstrated its potential to generate value in various applications and domains. From a data economy point of view, AI means algorithm-based and data-driven systems that enable machines with digital capabilities such as perception, reasoning, learning and even autonomous decision making to support people in real scenarios. Data ecosystems are an important driver for AI opportunities as they benefit from the significant growth of data volume and the rates at which it is generated. This chapter explores the opportunities and challenges of big data and AI in exploiting data ecosystems and creating AI value. The chapter describes the European AI framework as a foundation for deploying AI successfully and the critical need for a common European data space to power this vision.

scholarly journals GENERALIZED SPACE-TIME AUTOREGRESSIVE (GS-TAR) (Studi Kasus Peramalan Harga Saham Syariah Empat Perusahaan di JII)

Factor M ◽  
2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Sulis Setiya Ningsih
Keyword(s):  
Space Time ◽  
Time Model ◽  
Data Space ◽  
Diagnostic Checking ◽  
Space Time Model

Abstrak: Data deret waktu dari beberapa lokasi yang berdekatan seringkali mempunyai hubungan yang saling bergantung. Data yang tidak hanya mempunyai keterkaitan dengan kejadian pada waktu-waktu sebelumnya, tetapi juga mempunyai keterkaitan dengan lokasi lain disebut dengan data space-time. Model Generalized Space-Time Autoregrresive (GS-TAR) adalah suatu model yang banyak digunakan untuk memodelkan dan meramalkan data deret waktu dan lokasi. Adapun tujuan dari penelitian ini yaitu untuk mengaplikasikan model GS-TAR pada studi kasus memodelkan empat perusahaan yang termasuk saham syariah Jakarta Islamic Index (JII). Penelitian ini membahas tentang langkah-langkah analisis data runtun waktu dengan model GS-TAR. Metode ini terdiri dari beberapa tahap, yaitu uji stasioneritas, identifikasi model, estimasi parameter, diagnostic checking dan peramalan. Model runtun waktu GS-TAR (1;1) dapat melakukan peramalan harga saham syariah dengan baik. Hasil peramalan empat perusahaan yang diperoleh menunjukkan bahwa data dari hasil peramalan mendekati data aktual. Kata kunci : Model GS-TAR, Peramalan, Saham Syariah, Space-Time

scholarly journals Complex Big Data Analysis Based on Multi-granularity Generalized Functions

2018 ◽  
Vol 14 (04) ◽  
pp. 43
Author(s):  
Zhang Xueya ◽  
Jianwei Zhang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Model ◽  
Input Data ◽  
Big Data Analysis ◽  
Generalized Functions ◽  
Data Sets ◽  
Test Results ◽  
Data Space ◽  
Noise Data

<p>A new method for the big data analysis - multi-granularity generalized functions data model (referred to as MGGF for short) is put forward. This method adopts the dynamic adaptive multi-granularity clustering technique, transforms the grid like "Hard partitioning" to the input data space by the generalized functions data model (referred to as GFDM for short) into the multi-granularity partitioning, and identifies the multi-granularity pattern class in the input data space. By defining the type of the mapping relationship between the multi-granularity model class and the decision-making category ftype:Ci→y, and the concept of the Degree of Fulfillment (referred to as DoF (x)) of the input data to the classification rules of the various pattern classes, the corresponding MGGF model is established. Experimental test results of different data sets show that, compared with the GFDM method, the method proposed in this paper has better data summarization ability, stronger noise data processing ability and higher searching efficiency.</p>

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