Preemptive Security Through Information Analytics

ABSTRACT

Business security and threat actors continue to play a dangerous cat-and-mouse game with businesses intellectual property, customer data, and business reputations at stake. Businesses need to delve into a new way of doing business security to break out of this game. Businesses are sitting on repositories full of security-relevant data that is not being capitalized upon with the current information security and physical security organizations within businesses. This article poses the introduction of a data scientist role and a new supporting central data correlation technology platform based on big data predictive analytics into business security functions. The goal is to intelligently and autonomously identify, correlate and pinpoint normally innocuous or unnoticed security event attributes to allow security personnel to preemptively remediate physical and information risks before exploitation or loss of intellectual property occurs.     

Big Data Analytics Services for Enhancing Business Intelligence

This article examines how to use big data analytics services to enhance business intelligence (BI). More specifically, this article proposes an ontology of big data analytics and presents a big data analytics service-oriented architecture (BASOA), and then applies BASOA to BI, where our surveyed data analysis shows that the proposed BASOA is viable for enhancing BI and enterprise information systems. This article also explores temporality, expectability, and relativity as the characteristics of intelligence in BI. These characteristics are what customers and decision makers expect from BI in terms of systems, products, and services of organizations. The proposed approach in this article might facilitate the research and development of business analytics, big data analytics, and BI as well as big data science and big data computing.     

Business analytics and business value: A comparative case study

With growing adoption of business analytics, it is important for investing firms to understand how business value is created from investments. Studies in IT domain have highlighted how higher investment in technology may not bring more returns, rather how IT as an organizational capability acts as a key mediator in value creation. This research extends the model to business analytics, to identify elements of analytics technology assets and business analytics capability and to understand the mechanism of business value creation using multiple case studies. We capture how analytics resources contribute to business performance by developing operational and organizational performance measures.     

Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities

A central question for information systems (IS) researchers and practitioners is if, and how, big data can help attain a competitive advantage. To address this question, this study draws on the resource-based view, dynamic capabilities view, and on recent literature on big data analytics, and examines the indirect relationship between a firm’s big data analytics capability (BDAC) and competitive performance. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which, in turn, positively impact marketing and technological capabilities. To test our proposed research model, we used survey data from 202 chief information officers and IT managers working in Norwegian firms. By means of partial least squares structural equation modeling, results show that a strong BDAC can help firms build a competitive advantage. This effect is not direct but fully mediated by dynamic capabilities, which exerts a positive and significant effect on two types of operational capabilities: marketing and technological capabilities. The findings suggest that IS researchers should look beyond direct effects of big data investments and shift their attention on how a BDAC can be leveraged to enable and support organizational capabilities.     

A Conceptual Approach for Data Integration in Business Analytics

We present some basic concepts of a modelling environment for data integration in business analytics. Main emphasis is on defining a process model for the different activities occurring in connection with data integration, which allow later on assessment of the quality of the data. The model is based on combination of knowledge and techniques from statistical metadata management and from workflow processes. The modelling concepts are presented in a problem oriented formulation. The approach is embedded into an open model framework which aims for a modelling platform for all kinds of models useful in business applications.     

Business Analytics-Based Enterprise Information Systems

Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence.     

面向大数据分析的在线学习算法综述

大数据时代,越来越多的领域出现了对海量、高速数据进行实时处理的需求.如何对大数据流进行抽取转化成有用的信息并应用于各行各业变得越来越重要.传统的批量机器学习技术在大数据分析的应用中存在许多限制.在线学习技术采用流式计算模式,在内存中直接进行数据的实时计算,为流数据的学习提供了有利的工具.介绍了大数据分析的动机与背景,集中展示经典和最新的在线学习方法与算法,这种在线学习体系很有希望解决各种大数据挖掘任务面临的困难与挑战.主要技术内容包括3方面: 1) 线性模型在线学习;2) 基于核的非线性模型在线学习;3) 非传统的在线学习方法.各类方法尽量给出详细的模型和伪代码,讨论面向大数据分析的大规模机器学习研究与应用中的关键问题;给出大数据在线学习的3种典型应用场景,并探讨现今或将来在线学习领域进一步的研究方向.     

Apollo:Rapidly Picking the Optimal Cloud Configurations for Big Data Analytics Using a Data-Driven Approach

Big data analytics applications are increasingly deployed on cloud computing infrastructures,and it is still a big challenge to pick the optimal cloud configurations in a cost-effective way.In this paper,we address this problem with a high accuracy and a low overhead.We propose Apollo,a data-driven approach that can rapidly pick the optimal cloud configurations by reusing data from similar workloads.We first classify 12 typical workloads in BigDataBench by characterizing pairwise correlations in our offline benchmarks.When a new workload comes,we run it with several small datasets to rank its key characteristics and get its similar workloads.Based on the rank,we then limit the search space of cloud configurations through a classification mechanism.At last,we leverage a hierarchical regression model to measure which cluster is more suitable and use a local search strategy to pick the optimal cloud configurations in a few extra tests.Our evaluation on 12 typical workloads in HiBench shows that compared with state-of-the-art approaches,Apollo can improve up to 30％ search accuracy,while reducing as much as 50％ overhead for picking the optimal cloud configurations.     

Business value of big data analytics: A systems-theoretic approach and empirical test

Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.     

Business analytics in supply chains - The contingent effect of business process maturity

The paper analyzes the effect of the use of business analytics on supply chain performance. It investigates the changing information processing needs at different supply chain process maturity levels. The effects of analytics in each Supply Chain Operations Reference areas (Plan, Source, Make and Deliver) are analyzed with various statistical techniques. A worldwide sample of 788 companies from different industries is used. The results indicate the changing impact of business analytics use on performance, meaning that companies on different maturity levels should focus on different areas. The theoretical and practical implications of these findings are thoroughly discussed.     

Business intelligence and analytics use,innovation ambidexterity,and firm performance: A dynamic capabilities perspective

To survive in a dynamic and hyper-competitive business environment, firms are compelled to simultaneously introduce incremental and radical innovations. While it is recognised that business intelligence and analytics (BI&A) can support innovation and provide organisational value, the literature provides a limited understanding of its impact on balancing different innovation activities and ensuring performance gains. In this study, we examine the relationship between BI&A use, innovation ambidexterity, and firm performance by relying on the process theory of IS value creation as well as the dynamic capabilities perspective. We test our model using data collected from medium- and large-sized firms in Slovenia, applying partial least squares modelling. The results support the notion that BI&A use is positively associated with successful balancing between explorative and exploitative innovation activities, which in turn enhances firm performance. Our results also indicate that innovation ambidexterity is enhanced in two ways: indirectly through interaction with the firm’s absorptive capacity, and directly by increasing the possibilities of faster experimentation with offerings of products or services and improved predictability of the value of new products or services.     

Navi：基于自然语言交互的数据分析系统

随着大数据时代的到来，数据分析的作用日益显著，它能够从海量数据中发现有价值的信息，从而更有效地指导用户决策。然而，数据分析流程中存在三大挑战：分析流程高耦合、交互接口种类多和探索分析高耗时。为应对上述挑战，本文提出了基于自然语言交互的数据分析系统Navi，该系统采用模块化的设计原则，抽象出主流数据分析流程的三个核心功能模块：数据查询、可视化生成和可视化探索模块，从而降低系统设计的耦合度。同时，Navi以自然语言作为统一的交互接口，并通过一个任务调度器，实现了各功能模块的有效协同。此外，为了解决可视化探索中搜索空间指数级和用户意图不明确的问题，本文提出了一种基于蒙特卡洛树搜索的可视化自动探索方法，并设计了基于可视化领域知识的剪枝算法和复合奖励函数，提高了搜索效率和结果质量。最后，本文通过量化实验和用户实验验证了Navi的有效性。     

移动应用安全解析学:成果与挑战

随着移动应用(App)的广泛使用,移动应用的安全事件也频频发生。从数以亿计的移动应用中准确地识别出潜在的安全隐患成为了信息安全领域重要的难题之一。移动应用数量级增长的同时,也产生了海量的应用安全数据。这些数据使得移动应用的安全解析成为了可能。本文分别从用户界面解析、重打包应用检测、应用功能与安全行为一致性检测、基于上下文的恶意行为检测、终端用户应用管理和使用行为分析这五个方面介绍了移动应用安全解析学目前的成果。同时,基于以上的研究成果,对未来的研究方向进行了展望,并讨论了这些研究方向面临的挑战。     

Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective

This study draws on the sense-seize-transform view of dynamic capabilities as the theoretical lens for examining the role of BI&A in organizations. It views BI&A as the sensing and seizing components of dynamic capabilities that contribute to firm performance by enabling business process change. Findings confirm a positive relationship between BI&A and performance, mediated by business process change capabilities. This study answers the call for a theoretically grounded examination of the relationship between BI&A and firm performance by highlighting the significance of the BI&A seizing capabilities, and the importance of business process change in translating BI&A output into improved performance.     

Business analytics maturity of firms: an examination of the relationships between managerial perception of IT,business analytics maturity and success

This study develops a measure for business analytics (BA) maturity and empirically examines the relationships between managerial perception of IT, BA maturity and BA success. The findings suggest that (1) BA maturity can be measured via BA integration & management support, process-level benefits of BA and technology & data analytics capabilities, (2) BA maturity positively affects organizations’ overall BA success, and (3) managerial perception of IT positively influence organizations’ achievement of BA maturity.     

HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques

Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real-world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient solution. However, the problem is more fundamental, with many works emphasizing the importance of instance hardness. This issue refers to the significance of managing unsafe or potentially noisy instances that are more likely to be misclassified and serve as the root cause of poor classification performance. This paper introduces HardVis, a visual analytics system designed to handle instance hardness mainly in imbalanced classification scenarios. Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model. Additionally, rather than uniformly undersampling/oversampling a specific class, we allow users to find and sample easy and difficult to classify training instances from all classes. Users can explore subsets of data from different perspectives to decide all those parameters, while HardVis keeps track of their steps and evaluates the model's predictive performance in a test set separately. The end result is a well-balanced data set that boosts the predictive power of the ML model. The efficacy and effectiveness of HardVis are demonstrated with a hypothetical usage scenario and a use case. Finally, we also look at how useful our system is based on feedback we received from ML experts.     

Toward Data-Driven Digital Therapeutics Analytics: Literature Review and Research Directions

With the advent of digital therapeutics(DTx), the development of software as a medical device(SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products.To acquire a deeper understanding of DTx engagement and behavioral adherence, beyond efficacy, a large amount of contextual and interaction data from mobile and wearable devices during field deplo...     

Business intelligence success: The roles of BI capabilities and decision environments

This study examines the role of the decision environment in how well business intelligence (BI) capabilities are leveraged to achieve BI success. We examine the decision environment in terms of the types of decisions made and the information processing needs of the organization. Our findings suggest that technological capabilities such as data quality, user access and the integration of BI with other systems are necessary for BI success, regardless of the decision environment. However, the decision environment does influence the relationship between BI success and capabilities, such as the extent to which BI supports flexibility and risk in decision making.     

Big Data Analytics in Telecommunications: Literature Review and Architecture Recommendations

This paper focuses on facilitating state-of-the-art applications of big data analytics(BDA) architectures and infrastructures to telecommunications(telecom) industrial sector.Telecom companies are dealing with terabytes to petabytes of data on a daily basis. Io T applications in telecom are further contributing to this data deluge. Recent advances in BDA have exposed new opportunities to get actionable insights from telecom big data. These benefits and the fast-changing BDA technology landscape make it important to investigate existing BDA applications to telecom sector. For this, we initially determine published research on BDA applications to telecom through a systematic literature review through which we filter 38 articles and categorize them in frameworks, use cases, literature reviews, white papers and experimental validations. We also discuss the benefits and challenges mentioned in these articles. We find that experiments are all proof of concepts(POC) on a severely limited BDA technology stack(as compared to the available technology stack), i.e.,we did not find any work focusing on full-fledged BDA implementation in an operational telecom environment. To facilitate these applications at research-level, we propose a state-of-the-art lambda architecture for BDA pipeline implementation(called Lambda Tel) based completely on open source BDA technologies and the standard Python language, along with relevant guidelines.We discovered only one research paper which presented a relatively-limited lambda architecture using the proprietary AWS cloud infrastructure. We believe Lambda Tel presents a clear roadmap for telecom industry practitioners to implement and enhance BDA applications in their enterprises.     

Evolution of the impact of e-business technology on operational competence and firm profitability: A panel data investigation

This study examines the evolution of the impact of e-business technology on operational competence and profitability using a panel dataset of 154 Spanish firms. We find that (1) e-business technology has a positive effect on operational competence that decreases over time and (2) the firm’s proficiency in exploiting a portfolio of operational capabilities has a positive impact on profitability that becomes more substantial over time. The findings provide some insights on how the initial and subsequent IT investments affect operational competence and profitability over time. This study methodologically illustrates how to perform a partial least squares estimation using panel data.