data analysis
Recently Published Documents


TOTAL DOCUMENTS

56204
(FIVE YEARS 32251)

H-INDEX

229
(FIVE YEARS 56)

2022 ◽  
Vol 9 (3) ◽  
pp. 0-0

Missing data is universal complexity for most part of the research fields which introduces the part of uncertainty into data analysis. We can take place due to many types of motives such as samples mishandling, unable to collect an observation, measurement errors, aberrant value deleted, or merely be short of study. The nourishment area is not an exemption to the difficulty of data missing. Most frequently, this difficulty is determined by manipulative means or medians from the existing datasets which need improvements. The paper proposed hybrid schemes of MICE and ANN known as extended ANN to search and analyze the missing values and perform imputations in the given dataset. The proposed mechanism is efficiently able to analyze the blank entries and fill them with proper examining their neighboring records in order to improve the accuracy of the dataset. In order to validate the proposed scheme, the extended ANN is further compared against various recent algorithms or mechanisms to analyze the efficiency as well as the accuracy of the results.


2022 ◽  
Vol 31 ◽  
pp. 100790
Author(s):  
Sandra Martín-Torres ◽  
Laura Ruiz-Castro ◽  
Ana M. Jiménez-Carvelo ◽  
Luis Cuadros-Rodríguez

2022 ◽  
Vol 29 (1) ◽  
pp. 1-28
Author(s):  
Eunice Jun ◽  
Melissa Birchfield ◽  
Nicole De Moura ◽  
Jeffrey Heer ◽  
René Just

Data analysis requires translating higher level questions and hypotheses into computable statistical models. We present a mixed-methods study aimed at identifying the steps, considerations, and challenges involved in operationalizing hypotheses into statistical models, a process we refer to as hypothesis formalization . In a formative content analysis of 50 research papers, we find that researchers highlight decomposing a hypothesis into sub-hypotheses, selecting proxy variables, and formulating statistical models based on data collection design as key steps. In a lab study, we find that analysts fixated on implementation and shaped their analyses to fit familiar approaches, even if sub-optimal. In an analysis of software tools, we find that tools provide inconsistent, low-level abstractions that may limit the statistical models analysts use to formalize hypotheses. Based on these observations, we characterize hypothesis formalization as a dual-search process balancing conceptual and statistical considerations constrained by data and computation and discuss implications for future tools.


2022 ◽  
Vol 69 (1) ◽  
pp. 1-83
Author(s):  
Mark Kaminski ◽  
Egor V. Kostylev ◽  
Bernardo Cuenca Grau ◽  
Boris Motik ◽  
Ian Horrocks

Motivated by applications in declarative data analysis, in this article, we study Datalog Z —an extension of Datalog with stratified negation and arithmetic functions over integers. This language is known to be undecidable, so we present the fragment of limit Datalog Z programs, which is powerful enough to naturally capture many important data analysis tasks. In limit Datalog Z , all intensional predicates with a numeric argument are limit predicates that keep maximal or minimal bounds on numeric values. We show that reasoning in limit Datalog Z is decidable if a linearity condition restricting the use of multiplication is satisfied. In particular, limit-linear Datalog Z is complete for Δ 2 EXP and captures Δ 2 P over ordered datasets in the sense of descriptive complexity. We also provide a comprehensive study of several fragments of limit-linear Datalog Z . We show that semi-positive limit-linear programs (i.e., programs where negation is allowed only in front of extensional atoms) capture coNP over ordered datasets; furthermore, reasoning becomes coNEXP-complete in combined and coNP-complete in data complexity, where the lower bounds hold already for negation-free programs. In order to satisfy the requirements of data-intensive applications, we also propose an additional stability requirement, which causes the complexity of reasoning to drop to EXP in combined and to P in data complexity, thus obtaining the same bounds as for usual Datalog. Finally, we compare our formalisms with the languages underpinning existing Datalog-based approaches for data analysis and show that core fragments of these languages can be encoded as limit programs; this allows us to transfer decidability and complexity upper bounds from limit programs to other formalisms. Therefore, our article provides a unified logical framework for declarative data analysis which can be used as a basis for understanding the impact on expressive power and computational complexity of the key constructs available in existing languages.


2022 ◽  
Vol 30 (7) ◽  
pp. 0-0

To solve the dilemma between the increasing demand for cross-border e-commerce talents and incompatible students’ skill level, Industry-University-Research cooperation, as an essential pillar for inter-disciplinary talent cultivation model adopted by colleges and universities, brings out the synergy from relevant parties and builds the bridge between the knowledge and practice. Nevertheless, industry-university-research cooperation developed lately in the cross-border e-commerce field with several problems such as unstable collaboration relationships and vague training plans.


2022 ◽  
Vol 30 (2) ◽  
pp. 0-0

This research examines the important concept of transnational digital entrepreneurship (TDE). The paper integrates the host and home country entrepreneurial ecosystems with the digital ecosystem to the framework of the transnational digital entrepreneurial ecosystem. The authors argue that cross-border e-commerce platforms provide critical foundations in the digital entrepreneurial ecosystem. Entrepreneurs who count on this ecosystem are defined as transnational digital entrepreneurs. Interview data were dissected for the purpose of case studies to make understanding from twelve Chinese immigrant entrepreneurs living in Australia and New Zealand. The results of the data analysis reveal that cross-border entrepreneurs are in actual fact relying on the significant framework of the transnational digital ecosystem. Cross-border e-commerce platforms not only play a bridging role between home and host country ecosystems but provide entrepreneurial capitals as digital ecosystem promised.


2022 ◽  
Vol 30 (2) ◽  
pp. 1-19
Author(s):  
Carson Duan ◽  
Bernice Kotey ◽  
Kamaljeet Sandhu

This research examines the important concept of transnational digital entrepreneurship (TDE). The paper integrates the host and home country entrepreneurial ecosystems with the digital ecosystem to the framework of the transnational digital entrepreneurial ecosystem. The authors argue that cross-border e-commerce platforms provide critical foundations in the digital entrepreneurial ecosystem. Entrepreneurs who count on this ecosystem are defined as transnational digital entrepreneurs. Interview data were dissected for the purpose of case studies to make understanding from twelve Chinese immigrant entrepreneurs living in Australia and New Zealand. The results of the data analysis reveal that cross-border entrepreneurs are in actual fact relying on the significant framework of the transnational digital ecosystem. Cross-border e-commerce platforms not only play a bridging role between home and host country ecosystems but provide entrepreneurial capitals as digital ecosystem promised.


2022 ◽  
Vol 16 (4) ◽  
pp. 1-19
Author(s):  
Fei Gao ◽  
Jiada Li ◽  
Yisu Ge ◽  
Jianwen Shao ◽  
Shufang Lu ◽  
...  

With the popularization of visual object tracking (VOT), more and more trajectory data are obtained and have begun to gain widespread attention in the fields of mobile robots, intelligent video surveillance, and the like. How to clean the anomalous trajectories hidden in the massive data has become one of the research hotspots. Anomalous trajectories should be detected and cleaned before the trajectory data can be effectively used. In this article, a Trajectory Evaluator by Sub-tracks (TES) for detecting VOT-based anomalous trajectory is proposed. Feature of Anomalousness is defined and described as the Eigenvector of classifier to filter Track Lets anomalous trajectory and IDentity Switch anomalous trajectory, which includes Feature of Anomalous Pose and Feature of Anomalous Sub-tracks (FAS). In the comparative experiments, TES achieves better results on different scenes than state-of-the-art methods. Moreover, FAS makes better performance than point flow, least square method fitting and Chebyshev Polynomial Fitting. It is verified that TES is more accurate and effective and is conducive to the sub-tracks trajectory data analysis.


Sign in / Sign up

Export Citation Format

Share Document