Learning from Failure: Big Data Analysis for Detecting the Patterns of Failure in Innovative Startups [0.03%]
从失败中学习：大数据分析在创新型创业企业失败模式中的应用研究
Maddalena Cavicchioli,Ulpiana Kocollari
Maddalena Cavicchioli
This article aims at identifying appropriate models for analyzing large datasets to serve a twofold goal: first, to better understand the dynamics impacting innovative startups' performance and their managerial practice and, second, to dete...
Toward Business Process Innovation in the Big Data Era: A Mediating Roles of Big Data Knowledge Management [0.03%]
大数据时代的企业流程创新：大数据知识管理的中介作用研究
Saide Saide,Margaret L Sheng
Saide Saide
While recent debate recognizes the importance of big data (BD) and knowledge management (KM) in firm performance, there has been a paucity of literature regarding big data analytics technological (BDAT) and knowledge exploration-exploitatio...
John Darrell Van Horn
John Darrell Van Horn
Brain scientists are now capable of collecting more data in a single experiment than researchers a generation ago might have collected over an entire career. Indeed, the brain itself seems to thirst for more and more data. Such digital info...
Analyzing the Importance of Broker Identities in the Limit Order Book Through Deep Learning [0.03%]
基于深度学习的做市商身份在限价委托簿中重要性分析
Samuel Ping-Man Choi,Yin-Hei Chan,Sze-Sing Lam et al.
Samuel Ping-Man Choi et al.
Limit order books (LOBs) have been widely adopted as a trading mechanism in global securities markets, and the degree of LOB transparency is one of the most studied topics in market design. In the past, this issue was mainly researched thro...
On the Unstructured Big Data Analytical Methods in Firms: Conceptual Model, Measurement, and Perception [0.03%]
企业中非结构化大数据分析方法：概念模型、测度及认知研究
Piotr Tarka,Elżbieta Jędrych
Piotr Tarka
Firms face challenging analytical tasks at the advent of a growing amount of unstructured big data (BD). These data lead to radical shifts in their analytical strategies and market insights. Yet, the particular types of analytical methods r...
Lutz Oettershagen,Nils M Kriege,Christopher Morris et al.
Lutz Oettershagen et al.
Many real-world graphs are temporal, for example, in a social network, persons only interact at specific points in time. This temporal information directs dissemination processes on the graph, such as the spread of rumors, fake news, or dis...
LTSpAUC: Learning Time-Series Shapelets for Partial AUC Maximization [0.03%]
LTSpAUC：学习时间序列形状以最大化部分AUC
Akihiro Yamaguchi,Shigeru Maya,Kohei Maruchi et al.
Akihiro Yamaguchi et al.
Shapelets are discriminative segments used to classify time-series instances. Shapelet methods that jointly learn both classifiers and shapelets have been studied in recent years because such methods provide both interpretable results and s...
Yan Liu,Sriraam Natarajan
Yan Liu
Call for Special Issue Papers: Big Data Analytics for Agricultural Disaster Management [0.03%]
专题论文征集：大数据分析在农业灾害管理中的应用
S Balamurugan (Lead Guest Editor),Bala Anand Muthu,Sheng-Lung Peng et al.
S Balamurugan (Lead Guest Editor) et al.