دانلود رایگان مقاله انگلیسی تجزیه و تحلیل کلان داده و هوش تجاری در صنعت - اشپرینگر 2017

1 Introduction

The pervasive nature of digital technologies as witnessed in industry, services and everyday life has given rise to an emergent, data-focused economy stemming from many aspects of human individual and business activity. The richness and vastness of these data are creating unprecedented research opportunities in several fields including urban studies, geography, economics, finance, and social science, as well as physics, biology and genetics, public health and many others. Big data is the term for a collection of large and complex datasets from different sources that are difficult to process using traditional data management and processing applications. Big data is the description of a large amount of either organized or unorganized data that is analyzed to make an informed decision or evaluation. The data can be taken from a large variety of sources including browsing history, geolocation, social media, purchase history and medical records. Big data consists of complex data that would overwhelm the processing power of traditional simple database systems (Hung 2016).

2 Papers in the Special Issue

In this special issue, most of the papers assume that (1) the data sources are stored in Cloud (Ren et al. 2015); and (2) the data sources should be encrypted before outsourcing to Cloud storage for privacy requirements, which makes the traditional and efficient plain text keyword search technique useless (Fu et al. 2015; Xia et al. 2016). Referring to the first paper BRevealing Determinant Factors for Early Breast Cancer Recurrence by Decision Tree,^ by Guo et al. (2017) present a Decision Tree algorithm to the clinical information of a cohort of non-metastatic invasive breast cancer patients, to establish a classifier that categorizes patients based on whether they develop early recurrence and on similarities of their clinical and pathological diagnoses. As a result, this paper identified pathological nodal stage, the percentage of intra-tumor stroma and components of TGFß-Smad signaling pathway as highly relevant factors for early breast cancer recurrence.

Next, the second paper BGrammatical facial expression recognition in sign language discourse: a study at the syntax level,^ by Freitas et al. (2017) present a study which applies inductive reasoning to recognize patterns, to study the problem involving the automated recognition of Grammatical Facial Expressions (GFEs) at the discourse syntactic level in 1230 Inf Syst Front (2017) 19:1229–1232 the Libras (Brazilian) Sign Language. In general, GFEs stand out in automated recognition processes for sign languages, as they help to remove ambiguity among signals, and they also contribute to compose the semantic meaning of discourse.