检索结果-内蒙古大学图书馆

Data-driven prescriptive analytics applications: A comprehensive survey

Information Systems 2025年 134卷

作者： Martin Moesmann Torben Bach Pedersen Department of Computer Science Aalborg University Selma Lagerløfs Vej 300 Aalborg Ø DK-9220 Denmark

prescriptive analytics (PSA), an emerging business analytics field suggesting concrete options for solving business problems, has seen an increasing amount of interest after more than a decade of multidisciplinary research. This paper is a comprehensive survey of existing applications within PSA in terms of their use cases, methodologies, and possible future research directions. To ensure a manageable scope, we focus on PSA applications that develop data-driven, automatic workflows, i.e., Data-Driven PSA (DPSA) . Following a systematic methodology, we identify and include 104 papers in our survey. As our key contributions, we derive a number of novel taxonomies of the field and use them to analyse the field’s temporal development. In terms of use cases, we derive 10 application domains for DPSA, from Healthcare to Manufacturing, and subsumed problem types within each. In terms of individual method usage, we derive 5 method types and map them to a comprehensive taxonomy of method usage within DPSA applications, covering mathematical optimization , data mining and machine learning , probabilistic modelling , domain expertise , as well as simulations . As for combined method usage, we provide a statistical overview of how different method usage combinations are distributed and derive 2 generic workflow patterns along with subsumed workflow patterns, combining methods by either sequential or simultaneous relationships. Finally, we derive 5 possible research directions based on frequently recurring issues among surveyed papers, suggesting new frontiers in terms of methods, tools, and use cases.

关键词： prescriptive analytics Survey Application Optimization Machine learning Big data

来源：评论

学校读者我要写书评

暂无评论

Topical prescriptive analytics System for Automatic Recommendation of Convergence Technology

引用

BIOTECHNOLOGY AND BIOPROCESS ENGINEERING 2019年第6期24卷 893-906页

作者： Jeong, Do-Heon Joo, Hwang-Soo Duksung Womens Univ Dept Lib & Informat Sci Seoul 01369 South Korea Duksung Womens Univ Dept Biotechnol Seoul 01369 South Korea

This study applies text mining in scientific articles for discovery of interdisciplinary convergence technology between biotechnology (BT) and information and communication technology (ICT). For in-depth interpretation of the technologies without domain experts' review, a topic modeling method, Latent Dirichlet allocation (LDA), was used to propose an automatic recommendation system. We also applied prescriptive analytics with an option for users to select appropriate recommendation process of items. Our findings are as follows. First, LDA was efficient to facilitate the analysis of a large collection of documents by decreasing the dimension of the data. Second, the automatic recommendation method with various selectable options that could overcome limitations from that domain experts review the entire set of numerous topics. Finally, as a result of investigation of the final convergence technology candidates, it was proved that the system we propose here is more cost/time-effective compared to a method of reviewing all of the topic associations. Overall, a new methodology to support experts' final decision by LDAand prescriptive analytics-based automatic recommendation system was successfully developed to discover convergence technologies between BT and ICT, which was also proved by several examples of applications.

关键词： convergence technology topic modeling Latent Dirichlet allocation prescriptive analytics recommendation system

来源：评论

学校读者我要写书评

暂无评论

HadoopSec 2.0: prescriptive analytics-based multi-model sensitivity-aware constraints centric block placement strategy for Hadoop

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第6期39卷 8477-8486页

作者： Revathy, P. Mukesh, Rajeswari Hindustan Inst Technol & Sci Dept Comp Sci & Engn Chennai Tamil Nadu India

Like many open-source technologies such as UNIX or TCP/IP, Hadoop was not created with Security in mind. Hadoop however evolved from the other tools over time and got widely adopted across large enterprises. Some of Hadoop's architectural features present Hadoop its unique security issues. Given this security vulnerability and potential invasion of confidentiality due to malicious attackers or internal customers, organizations face challenges in implementing a strong security framework for Hadoop. Furthermore, given the method in which data is placed in Hadoop Cluster adds to the only growing list of these potential security vulnerabilities. Data privacy is compromised when these critical and data-sensitive blocks are accessed either by unauthorized users or for that matter even misuse by authorized users. In this paper, we intend to address the strategy of data block placement across the allotted DataNodes. prescriptive analytics algorithms are used to determine the Sensitivity Index of the Data and thereby decide on data placement allocation to provide impenetrable access to an unauthorized user. This data block placement strategy aims to adaptively distribute the data across the cluster using innovative ML techniques to make the data infrastructure extra secured.

关键词： Big data Hadoop security data placement strategy for sensitive data sensitivity in Hadoop prescriptive analytics

来源：评论

学校读者我要写书评

暂无评论

prescriptive analytics Through Constrained Bayesian Optimization 22nd

Prescriptive Analytics Through Constrained Bayesian Optimiza...

引用

22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Harikumar, Haripriya Rana, Santu Gupta, Sunil Thin Nguyen Kaimal, Ramachandra Venkatesh, Svetha Deakin Univ Ctr Pattern Recognit & Data Analyt Geelong Vic Australia Amrita Univ Comp Sci & Engn Dept Kollam India Amrita Univ Amrita CREATE Kollam India

ISBN: (纸本)9783319930343;9783319930336

prescriptive analytics leverages predictive data mining algorithms to prescribe appropriate changes to alter a predicted outcome of undesired class to a desired one. As an example, based on the conversation of a reformed addict on a message board, prescriptive analytics may predict the intervention required. We develop a novel prescriptive analytics solution by formulating a constrained Bayesian optimization problem to find the smallest change that we need to make on an actionable set of features so that with sufficient confidence an instance can be changed from an undesirable class to the desirable class. We use two public health dataset, multi-year CDC dataset on disease prevalence across the 50 states of USA and alcohol related data from Reddit to demonstrate the usefulness of our results.

关键词： prescriptive analytics Bayesian optimization Linear and nonlinear classifiers Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

prescriptive analytics System for Long-Range Aircraft Conflict Detection and Resolution 18

Prescriptive Analytics System for Long-Range Aircraft Confli...

引用

26th ACM-SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS)

作者： Ayhan, Samet Costas, Pablo Samet, Hanan Univ Maryland College Pk MD 20742 USA Boeing Res & Technol Europe Madrid Spain Boeing Res & Technol Madrid Spain

ISBN: (纸本)9781450358897

At the present time, there is no mechanism for Air Navigation Service Providers (ANSPs) to probe new flight plans filed by the Airlines Operation Centers (AOCs) against the existing approved flight plans to see if they are likely to cause conflicts or bring sector traffic densities beyond control. In the current Air Traffic Control (ATC) operations, aircraft conflicts and sector traffic densities are resolved tactically, increasing workload and leading to potential safety risks and loss of capacity and efficiency. We propose a novel prescriptive analytics System to address a long-range aircraft conflict detection and resolution (CDR) problem. Given a set of predicted trajectories, the system declares a conflict when a protected zone of an aircraft on its trajectory is infringed upon by another aircraft. The system resolves the conflict by prescribing an alternative solution that is optimized by perturbing at least one of the trajectories involved in the conflict. To achieve this, the system learns from descriptive patterns of historical trajectories and pertinent weather observations and builds a Hidden Markov Model (HMM). Using a variant of the Viterbi algorithm, the system avoids the airspace volume in which the conflict is detected and generates a new optimal trajectory that is conflict-free. The key concept upon which the system is built is the assumption that airspace is nothing more than horizontally and vertically concatenated set of spatio-temporal data cubes where each cube is considered as an atomic unit. We evaluate our system using real trajectory datasets with pertinent weather observations from two continents and demonstrate its effectiveness for strategic CDR.

关键词： prescriptive analytics Hidden Markov Model Time Series

来源：评论

学校读者我要写书评

暂无评论

prescriptive analytics in airline operations: Arrival time prediction and cost index optimization for short-haul flights

引用

OPERATIONS RESEARCH PERSPECTIVES 2018年 5卷 265-279页

作者： Achenbach, Anna Spinler, Stefan WHU Otto Beisheim Sch Management Kuehne Fdn Endowed Chair Logist Management Burgpl 2 D-56179 Vallendar Germany

In this paper, we provide arrival time prediction combined with a cost index optimization model for short haul flights. Our work is based on flight data of a European network carrier. We focus on predicting the arrival time for incoming flights at two hub locations. Airlines focus on two aspects in their operations: Minimizing cost while ensuring on-time arrivals. Especially network carriers with hub connections need to ensure that incoming flights are on time for passenger, crew and aircraft transfer. The cost index is a tool for optimizing the aircraft's speed. A high cost index implies a faster flight. The cost of time is set in relation to the cost of fuel. Today there is no model for arrival time prediction and integrated cost index optimization. We consider three different flight distances to model the impact of cost index changes on gate arrival time. With our model airlines are able to reduce the cost index without any tangible impact on their overall schedule. We conclude that the optimal cost index level heavily depends on a flight's distance, fuel costs and delay costs. Especially for short haul flights we recommend lowering the cost index as a high cost index has limited impact on gate arrival time.

关键词： OR in airlines analytics Aircraft arrival time prediction prescriptive analytics Cost index optimization

来源：评论

学校读者我要写书评

暂无评论

Optimizing outpatient appointment system using machine learning algorithms and scheduling rules: A prescriptive analytics framework

引用

EXPERT SYSTEMS WITH APPLICATIONS 2018年 102卷 245-261页

作者： Srinivas, Sharan Ravindran, A. Ravi Univ Missouri Dept Ind & Mfg Syst Engn Coll Engn Columbia MO 65211 USA Univ Missouri Dept Mkt Trulaske Coll Business Columbia MO 65211 USA Penn State Univ Dept Ind & Mfg Engn University Pk PA 16802 USA

In the US, the demand for outpatient services is expected to increase, while the supply of physicians to provide the care is projected to decrease. Besides, inefficiencies in the appointment system (AS) and patient no-shows (patients who do not arrive for scheduled appointments) reduce provider productivity, timely access to care, and cost the U.S. healthcare system more than $150 billion a year. To handle increasing demand and compensate for patient no-shows, outpatient clinics tend to overbook appointments. The current scheduling practice at most clinics and majority of the scheduling rules proposed in the literature assume all patients are equally likely to miss an appointment. Further, most scheduling rules in the literature do not make use of the available data, such as electronic health records, when scheduling patients. This paper proposes a prescriptive analytics framework to improve the performance of an AS with respect to patient satisfaction (measured using average patient waiting time and number of patients unable to get an appointment for the day under consideration) and resource utilization (measured using average resource idle time, overflow time and overtime). In the proposed framework, patient-related data from various sources are used to develop predictive models that identify the risk of a patient no-show. Different scheduling rules, that leverage the patient-specific no-show risk is then proposed. A case study, with real data from a Family Medicine Clinic in Pennsylvania, is used to show the feasibility of the proposed framework. The effectiveness of the proposed scheduling rules is evaluated by benchmarking it with three rules adapted from the literature. The results indicate that the proposed scheduling rules consistently outperform the benchmark rules for all the clinic settings tested. Further, the proposed framework is generic and can be adopted by any outpatient clinic characterized by occurrences of no-shows and appointment-based customer

关键词： Patient scheduling Patient-specific no-shows Sequential scheduling rules Machine learning prescriptive analytics

来源：评论

学校读者我要写书评

暂无评论

Differentially Private prescriptive analytics 18

Differentially Private Prescriptive Analytics

引用

18th IEEE International Conference on Data Mining Workshops (ICDMW)

作者： Harikumar, Haripriya Rana, Santu Gupta, Sunil Thin Nguyen Kaimal, Ramachandra Venkatesh, Svetha Deakin Univ Ctr Pattern Recognit & Data Analyt Geelong Vic Australia Amrita Univ Amrita CREATE Kollam India Amrita Univ Comp Sci & Engn Dept Kollam India

ISBN: (纸本)9781538691595

Privacy preservation is important. prescriptive analytics is a method to extract corrective actions to avoid undesirable outcomes. We propose a privacy preserving prescriptive analytics algorithm to protect the data used during the construction of the prescriptive analytics algorithm. We use differential privacy mechanism to achieve strong privacy guarantee. Differential privacy mechanism requires computation of sensitivity: maximum change in the output between two training datasets, which is differed by only one instance. The main challenge we addressed is the computation of sensitivity of the prescription vector. In absence of any analytical form, we construct a nested global optimization problem to compute the sensitivity. We solve the optimization problem using constrained Bayesian optimization, as the nested structure makes the objective function expensive. We demonstrate our algorithm on two real world datasets and observe that the prescription vectors remains useful even after making them private.

关键词： Differential Privacy prescriptive analytics Laplace distribution Bayesian optimization Constrained optimization

来源：评论

学校读者我要写书评

暂无评论

prescriptive analytics for FIFA World Cup lodging capacity planning

引用

JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2017年第10期68卷 1183-1194页

作者： Ghoniem, Ahmed Ali, Agha Iqbal Al-Salem, Mohammed Khallouli, Wael Univ Massachusetts Isenberg Sch Management Dept Operat & Informat Management Amherst MA 01003 USA Qatar Univ Dept Mech & Ind Engn Doha Qatar

The FIFA World Cup, comprising sixty-four matches spanning an entire month, has, in recent years, been attended by about three million spectators of which over half a million are visitors requiring lodging. Planning lodging capacity for an event of this magnitude is necessary for host nations where pre-existing infrastructures are either inadequate or lacking. This paper develops an optimization analytics framework that sequentially employs two integer programming models for foreign spectator analysis and the consequent lodging requirements. The framework is applied to assess the preparedness of lodging infrastructure in Qatar for FIFA 2022.

关键词： capacity planning FIFA World Cup optimization prescriptive analytics

来源：评论

学校读者我要写书评

暂无评论

PrescStream: A Framework for Streaming Soft Real-Time Predictive and prescriptive analytics 17th

PrescStream: A Framework for Streaming Soft Real-Time Predic...

引用

17th International Conference on Computational Science and its Applications (ICCSA)

作者： de Aguiar, Marcos Greve, Fabiola Costa, Genaro Univ Fed Bahia Comp Sci Dept Salvador BA Brazil Univ Fed Bahia Humanities Arts & Sci Dept Salvador BA Brazil

ISBN: (纸本)9783319623924;9783319623917

All the data volume generated by modern applications brings opportunities for knowledge extraction and value creation. In this sense, the integration of predictive and prescriptive analytics may help the industry and users to be more productive and successful. It means not only to estimate an outcome but also to act on it in the real world. Nonetheless, mastering these concepts and providing their integration is not an easy task. This work proposes PrescStream, a proof of concept framework that uses machine learning based prediction, and process this outcome result to do prescriptive analytics, allowing researchers to integrate predictive and prescriptive analytics into their experiments. It has a scalable, fault-tolerant microservices based architecture, making it ideal for cloud deployment and IoT (internet of things) applications. The paper describes the general architecture of the system, as well as a validation usage with result analysis.

关键词： Predictive analytics prescriptive analytics Microservices Internet of things Architecture frameworks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：