Cost-Effective Acquisition of First-Party Data for Business Analytics

The rate of development of open government data (OGD) portals has been fast in recent years due to potential benefits of the utilization of OGD. However, scholars have emphasized lack of use as a key problem in the realization of these benefits. Although studies have been carried out to understand decisive factors in OGD utilization from the aspects of either portals or users, they failed to consider the interaction between the two. Therefore, our study carried out an analysis of the interaction between users and OGD portals during users’ data acquisition process from three aspects: data acquisition methods, data quality requirements, and helping functions. We carried out a survey in a Chinese population to collect data for analysis. Results show users’ high acceptance of keyword search as their method for data acquisition through OGD portals but browsing showed higher usage frequency and was a more stable data acquisition behavior. Females show better acceptance of regular recommendations (e.g. RSS) based on their visiting histories than males. Users’ age, education background and occupation affect their demands of different data quality attributes. Our analysis also shows positive relationship between users’ data acquisition habits with their demands of data quality, users’ need of help with their feelings of difficulties in using the portal, and users’ need of help with their demands of data quality. We suggest promoting OGD utilization by offering better helping functions and improving data qualities in future development of OGD portals.

Recently, a novel data acquisition method has been proposed and experimentally implemented for differential phase‐contrast computed tomography (DPC‐CT), in which a conventional X‐ray tube and a Talbot‐Lau‐type interferometer were utilized in data acquisition. The divergent nature of the data acquisition system requires a divergent‐beam image reconstruction algorithm for DPC‐CT. This paper focuses on addressing this image reconstruction issue. We have developed a filtered backprojection algorithm to directly reconstruct the DPC‐CT images from acquired fan‐beam data. The developed algorithm allows one to directly reconstruct the decrement of the real part of the refractive index from the measured data. In order to accurately reconstruct an image, the data need to be acquired over an angular range of at least 180∘ plus the fan angle. As opposed to the parallel beam data acquisition and reconstruction methods, a 180∘‐rotation angle for the data acquisition system does not provide sufficient data for an accurate reconstruction of the entire field of view. Numerical simulations have been conducted to validate the image reconstruction algorithm.

Model calibration is challenging for large-scale system models with a great number of variables. Existing approaches to partitioning system models and prioritizing data acquisition rely on heuristics rather than formal treatments. The sensor placement problem in physical dynamic systems points to a promising avenue for formalizing data acquisition priorities, which addresses the following question for system models: With the model at hand and pre-existing data availability on a subset of model variables, what are the (next) k model variables that would bring the largest utility to model calibration, once their data are acquired? In this study, we formalize this problem as a combinatorial optimization and adapt two solutions, the information-entropy approach and the miss-probability approach, from physical dynamic systems to system models in management sciences. Next, based on the idea of the data availability partition, we develop a third solution. The new approach can be understood from the entropy perspective and is embedded in the theoretical framework for the evaluation of side information. Our solution applies to system models of all topologies: analytical results of the optimal placement are derived for binary/multinary trees; for general (directed) tree structures, an algorithm to determine the optimal placement is devised, whose complexity is upper-bounded by [Formula: see text] for an n-variable system; for arbitrary model topologies with the presence of loops, sequential-optimal and simulated-annealing schemes are formulated. Three approaches are compared on a validating example; our solution outperforms the two alternatives. Application on a multicompartment system demonstrates the toolkit’s practical use. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0128 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0128 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Business analytics (BA) is revolutionizing how businesses use data to gain a competitive advantage. It offers opportunities for deeper customer insights, targeted marketing campaigns, improved customer satisfaction, new product and service opportunities, and optimized business processes. However, challenges like data quality, talent acquisition, and ethical considerations exist. This study reviews BA literature and discusses the challenges faced by organizations in implementing business analytics, including data availability, quality, skills and training, and technology infrastructure considerations. We conducted a study on 133 analytics professionals in Lahore, Pakistan. Data availability and quality, skills and training, and technology infrastructure all had a positive and significant impact on business analytics challenges and opportunities. The findings aim to contribute to a better understanding of business analytics, implementation challenges, and opportunities.

Strategies to cope with floods, for instance, integrated flood management, require proper flood hazard assessment. Such flood hazard assessment relies on the flood modelling approaches, which require high-quality and quantity data to produce realistic flood hazard maps. In many cities in the developing countries, also known as data scarce-areas, flood modelling is challenging as there is sparse or no observation on rainfall data and soil information used for proper model development, model calibration, and validation. However, open-source geospatial data and the NWP model rainfall product can overcome the data scarcity problem. Therefore, this Ph.D. thesis aimed to explore publicly available geospatial datasets and their integration with hydro-meteorological modelling systems to overcome the data-scarcity challenges, specifically, to explore the data of extreme rainfall, soil, and land-cover information for flash flood modelling in the urbanized catchment. The findings are summarized into three phases, as discussed in the following. The first phase of this Ph.D. study focuses on exploring soil information used for flash flood modelling in the urbanized catchment. Accordingly, the soil information that is determining the infiltration processes (i.e., Ksat, porosity, initial condition, soil matric suction, and soil depth) is derived following three different soil databases: (1) FAO soil map (SMFAO); (2) soil map derived based on the soil-landscape relationships (SMLS), and (3) soil map derived from the SoilGrids database (SMSG)). The soil information derived from these data sources is believed to be overcome the data limitation problem. However, the open-source soil databases cannot correctly consider the local features (e.g., wetlands, fragmented vegetation cover, and soil compaction), which would lead to the data quality problem. Therefore, in this study, the local features' influence on the derived soil information is numerically adjusted by incorporating the land cover data derived from the satellite image. The derived soil information is then used as the input to openLISEM integrated flood modelling system to assess their impact on flood dynamics in the urbanized catchment. The impact analysis is evaluated as the compacted and uncompacted soil condition. The results indicated that the flood dynamics are highly sensitive to different soil databases. The incorporation of soil compaction into the soil information has the largest impact on the flood dynamics in the catchment. This study showed that open-source data choice strongly influences both the simulated quantity and spatial variability of the infiltration, which directly affects runoff and flooding. On top land information in the context of Rwanda: 1) the hilly terrain, 2) UAV regulations, 3) ground-truthing in urban areas, 4) data processing capacities at the national land administration authority. In sum, although UAVs can play a key role in satisfying stakeholder needs, structural and capacity conditions currently present major challenges to exploit the potential of UAV data acquisition workflows. Triggered by tremendous regulatory obstacles to fly UAVs in Eastern Africa, the second part of this thesis analyses the interface between UAV technology and stakeholders in terms of legal and regulatory frameworks. To get a better understanding of past, present and future developments of UAV regulations, a thorough literature review and a comparative analysis of national UAV regulations was conducted. Although all UAV regulations have a similar target – minimizing the risk for people and property on the ground – a distinct heterogeneity of national regulatory frameworks was investigated. However, commonalities such as mandatory platform registration, obligatory insurance coverage and standard pilot licensing procedures indicate trends towards mature national UAV regulation. Desirable trends are seen in the coexistence of hard and soft regulations and the successful international dialogue that may eventually provide a legal framework for harmonized regulatory standards. The third part of this thesis explores the technical and operational aspect of UAV workflows and assesses different data quality measures with the aim to recommend optimal UAV data acquisition workflows. Imagery from six study areas across Europe and Africa, representing overall more than 100 different flight configurations, provided the basis for an integrated data quality assessment including various aspects of the photogrammetric processing chain. The results suggest that scene context, flight configuration, and GCP setup significantly impact the final data quality and subsequent automatic delineation of visual cadastral boundaries. Moreover, even though the root mean square error of checkpoint residuals as a commonly accepted error measure lies within a range of few centimeters in all datasets, our results show large discrepancies of the accuracy and the completeness of automatically detected cadastral features for orthophotos generated from different flight plans. With its unique combination of methods and integration of various study sites, these results and recommendations can help land professionals and bottom-up initiatives alike to optimize existing and future UAV data collection workflows. Despite the prospects and market opportunities in land administration, there is a gap between experimentation and widespread diffusion of UAV technology. To understand the dynamics of the diffusion process of UAV technology as a tool for effective land administration, the fourth part of this thesis integrates the Framework for Effective Land Administration (FELA) and the Hype Cycle concept. The majority of experts estimated UAV technology to be in a phase in which the innovation needs to overcome initial unmet expectations to foster market development and increased adoption. The results indicate a changing importance of different FELA pathways during this process, led by alternating top-down and bottom-up dynamics. Enabling laws and policies in addition to supporting governance, accountability and institutions are found to be crucial to create a UAV-friendly national ecosystem early on in the process of technology adoption and allay exaggerated expectations. Once this ecosystem has been created, market demand is expected to surge driven by partnerships, adapted standards, tech advocacy and awareness-raising campaigns, highlighting the superiority of highresolution data amongst other benefits of UAV technology. These insights can be used as a baseline to direct national strategic decisions towards the increased adoption of UAVs in land administration. In conclusion, the integrated view on the socio-technical system, including stakeholders, land administration processes and UAV technology as system elements, allows identifying interactions and positive and negative dependencies. Whereas the aspects of data quality and regulations both influence but are also impacted by other elements, land data needs were found to have a critical role in the system and predominantly affect other elements as a starting point for any land tool intervention. Representing the overlap of all three system elements, the diffusion of UAV technology in land administration takes a central place. Here, the superior quality of UAV data is a key driver throughout the diffusion process. In contrast, regulations may positively or negatively affect the emergence of UAV technology, as appropriate UAV legislation presents a vital pre-condition for technology uptake. Ultimately and potentially most importantly, land data needs stimulate the scaled implementation creating a demand for up-to-date high-resolution land data. Overall, the findings of this thesis suggest that UAV technology is a highly flexible, widely available, easy-to-use and end-user responsive data acquisition tool that complements the range of mapping tools for land administration processes side by side with satellite data and GNSS surveying. Yet, regulatory constraints, missing awareness and weak governance support currently hamper exploiting the full potential of UAV technology for land administration in many countries. of that, the effect of sealing and compaction is equally essential and nearly outweighs the differences caused by the use of different soil databases. The second part of the Ph.D. thesis is to model and analyze the high-intensity rainfall product using the WRF model, which combined two main research objectives (chap.3 & 4 in the document). The first objective is to evaluate the satellite-driven urban fraction appropriateness in the WRF model for simulating high-intensity rainfall events in urbanized areas. Three different simulations are performed in order to distill the impact of changing urban fractions and adjusted urban parameters on the simulated rainfall: The first simulation (1) StandardWPS, which is carried out using the default urban fraction with the default urban parameters; (2) UFD_Parameter, which is using the default urban fraction, but with adjusted urban parameters; and (3) Updated, which is with the updated urban fraction based on the Landsat 2016 image and the adjusted urban parameters. All model simulations are configured at high spatial (1 km) and temporal (10-minute) resolutions forced with the latest ERA5 global reanalysis dataset. The model result was validated using the rainfall observation from the gauging station and CHIRPS data. The results showed that the simulated rainfall performs better with a relatively lower error when using the updated urban fraction. The satellite-derived urban map represents a more realistic extent and intensity of the urban fraction with a heterogeneous urban fraction, which results in more realistic rainfall simulations. The second objective of the second part of the Ph.D. is to evaluate the suitability of the WRF model in simulating high-intensity rainfall events. Here, we evaluated the pro

Despite the increasing availability of biodiversity data, determing the quality of data and informing would-be data consumers and users remains a significant issue. In order for data users and data owners to perform a satisfactory assessment and management of data fitness for use, they require a Data Quality (DQ) report, which presents a set of relevant DQ measures, validations, and amendments assigned to data. Determining the meaning of "fitness for use" is essential to best manage and assess DQ. To tackle the problem, the TDWG Biodiversity Data Quality (BDQ) - Interest Group (IG) (https://github.com/tdwg/bdq) has proposed a conceptual framework that defines the necessary components to describe Data DQ needs, DQ solutions, and DQ reports (Fig. 1). It supports, in a global and collaborative environment, a consistent description of: (1) the meaning of data fitness for use in specific contexts, using the concept of a DQ profile; (2) DQ solutions, using the concepts of specifications and mechanisms; and (3) the status of quality of data according to a DQ profile, using the concept of a DQ report (Veiga 2016, Veiga et al. 2017). Based on this this conceptual framework, we implemented a prototype of a Fitness for Use Backbone (FFUB) as a Node.js module (https://nodejs.org/api/modules.html) for registering and retrieving instances of the framework concepts. This prototype was built using Node.js, an asynchronous event-driven JavaScript runtime, which uses a non-blocking I/O model that makes it lightweight and efficient to build scalable network applications (https://nodejs.org). We registered our module in the npm package manager (https://www.npmjs.com) in order to facilitate its reuse and we made our source code available in GitHub (https://github.com) in order to foster collaborative development. To test the module, we developed a simple mechanism for measuring, validating and amending the quality of datasets and records, called BDQ-Toolkit, available in the FFUB module. The source code of the FFUB module can be found at https://github.com/BioComp-USP/ffub. Installing and using the module requires Node.js version 6 or higher. Instructions for installing and using the FFUB module can be found at https://www.npmjs.com/package/ffub (Veiga and Saraiva 2017). Using the FFUB module we defined a simple DQ profile describing the meaning of data fitness for use in a specific context by registering a hypothetical use case. Then, we registered a set of valuable information elements for the context of the use case. For measuring the quality of each valuable information elements, we registered a set of DQ dimensions. To validate if the DQ measures are good enough, a set of DQ criteria was defined and registered. Lastly, a set of DQ enhancements for amending the quality in the use case context was also defined and registered. In order to describe the DQ solution used to meet those DQ needs, we registered the BDQ-Toolkit mechanism and all the specifications implemented by it. Using these specifications and mechanism, we generated and assigned to a dataset and its records a set of DQ assertions, according to the DQ dimensions, criteria and enhancements defined in the DQ profile. Based on those assertions we can build DQ reports by composing all the assertions assigned to the dataset or to a specific record. This DQ report describes the status of DQ of a dataset or record according to the context of the DQ profile. This module provides an interface to use the proposed conceptual framework, which allows others to register instances of its concepts. Future work will include creating a RESTful API using sophisticated methods of data retrieval.

Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on a black-box numerical code or real-world experiment, which we refer to as a simulation. Whereas an extensive body of research is dedicated to developing new algorithms and methods for solving these and related problems, it is challenging and time-consuming to integrate these techniques into real-world production-ready solvers. This is partly because of the diversity and complexity of modern state-of-the-art MOSO algorithms and methods and partly because of the complexity and specificity of many real-world problems and their corresponding computing environments. The complexity of this problem is only compounded when introducing potentially complex and/or domain-specific surrogate-modeling techniques, problem formulations, design spaces, and data acquisition functions. This paper carefully surveys the current state of the art in MOSO algorithms, techniques, and solvers, as well as problem types and computational environments where MOSO is commonly applied. We then present several key challenges in the design of a parallel multiobjective simulation optimization framework (ParMOO) and how they have been addressed. Finally, we provide two case studies demonstrating how customized ParMOO solvers can be quickly built and deployed to solve real-world MOSO problems. History: Accepted by Alice Smith and Ted Ralphs, Area Editors for Software Tools. Funding: This work was supported by US DOE, Office of Science, Advanced Scientific Computing Research, SciDAC Program [Grants DE-AC02-05CH11231 and DE-AC02-06CH11357]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0250 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0250 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

With the rapid development of new technologies such as artificial intelligence, big data, and the Internet of Things, many researchers have probed into the study of learning analysis, trying to solve the problems of teaching by analyzing the learning behavior data from learning process. And in many learning behavior research, the sensor network usually consists of a host of mutually independent data sources, which can be used to monitor measured objects from multiple dimensions thereby obtaining the multi-source multi-modal sensory data. However, there still exist false negative readings, false positive readings and environmental interference, etc. Therefore, we propose a multi-source multimode sensory data acquisition method based on Date Quality(DQ). We first define the data quality in terms of four aspects-accuracy, integrity, consistency and instantaneity. Then, by the modeling there aspects respectively, we propose metrics to estimate the comprehensive data quality method of multi-source multi-mode sensory data. Finally, a data acquisition method is presented based on data quality, which selects a part of data sources for data transmission according to the given precision. This method aims at reducing the consumption of the sensory network on the premise of the data quality guarantee. An extensive experimental evaluation demonstrates the efficiency and effectiveness of the algorithm.

PurposeThis study addresses the need for a theory-guided, rich, descriptive account of research data repositories' (RDRs) understanding of data quality and the structures of their data quality assurance (DQA) activities. Its findings can help develop operational DQA models and best practice guides and identify opportunities for innovation in the DQA activities.Design/methodology/approachThe study analyzed 122 data repositories' applications for the Core Trustworthy Data Repositories, interview transcripts of 32 curators and repository managers and data curation-related webpages of their repository websites. The combined dataset represented 146 unique RDRs. The study was guided by a theoretical framework comprising activity theory and an information quality evaluation framework.FindingsThe study provided a theory-based examination of the DQA practices of RDRs summarized as a conceptual model. The authors identified three DQA activities: evaluation, intervention and communication and their structures, including activity motivations, roles played and mediating tools and rules and standards. When defining data quality, study participants went beyond the traditional definition of data quality and referenced seven facets of ethical and effective information systems in addition to data quality. Furthermore, the participants and RDRs referenced 13 dimensions in their DQA models. The study revealed that DQA activities were prioritized by data value, level of quality, available expertise, cost and funding incentives.Practical implicationsThe study's findings can inform the design and construction of digital research data curation infrastructure components on university campuses that aim to provide access not just to big data but trustworthy data. Communities of practice focused on repositories and archives could consider adding FAIR operationalizations, extensions and metrics focused on data quality. The availability of such metrics and associated measurements can help reusers determine whether they can trust and reuse a particular dataset. The findings of this study can help to develop such data quality assessment metrics and intervention strategies in a sound and systematic way.Originality/valueTo the best of the authors' knowledge, this paper is the first data quality theory guided examination of DQA practices in RDRs.

This paper develops a cost-effective sequential route recommender system to provide real-time routing recommendations for vacant taxis searching for the next passenger. We propose a prediction-and-optimization framework to recommend the searching route that maximizes the expected profit of the next successful passenger pickup based on the dynamic taxi demand-supply distribution. Specifically, this system features a deep learning-based predictor that dynamically predicts the passenger pickup probability on a road segment and a recursive searching algorithm that recommends the optimal searching route. The predictor integrates a graph convolution network (GCN) to capture the spatial distribution and a long short-term memory (LSTM) to capture the temporal dynamics of taxi demand and supply. The GCN-LSTM model can accurately predict the pickup probability on a road segment with the consideration of potential taxi oversupply. Then, the dynamic distribution of pickup probability is fed into the route optimization algorithm to recommend the optimal searching routes sequentially as route inquiries emerge in the system. The recursion tree-based route optimization algorithm can significantly reduce the computational time and provide the optimal routes within seconds for real-time implementation. Finally, extensive experiments using Beijing Taxi GPS data demonstrate the effectiveness and efficiency of the proposed recommender system. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the Hong Kong Research Grants Council [Grants CityU 21500220, CityU 11504322] and the National Natural Science Foundation of China [Grant 72201222]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0112 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0112 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

We present a new difference of convex functions algorithm (DCA) for solving linear and nonlinear mixed complementarity problems (MCPs). The approach is based on the reformulation of bilinear complementarity constraints as difference of convex (DC) functions, more specifically, the difference of scalar, convex quadratic terms. This reformulation gives rise to a DC program, which is solved via sequential linear approximations of the concave term using standard DCA techniques. The reformulation is based on a generalization of earlier results on recasting bilinearities and leads to a novel algorithmic framework for MCPs. Through extensive numerical experimentation, the proposed approach, referred to as DCA-BL, proves to be an efficient heuristic for complementarity problems. For linear complementarity problems (LCPs), we test the approach on a number of randomly generated instances by varying the size, the density, and the eigenvalue distribution of the LCP matrix, providing insights into the numerical properties of DCA-BL. In addition, we apply the framework to a market equilibrium problem and find that DCA-BL scales well on realistic LCP instances. Also, through experimentation, we find that that DCA-BL performs particularly well compared with other DC-based complementarity approaches in the literature if the LCP is highly dense, asymmetric, or indefinite. Lastly, the method is successfully applied to a set of equilibrium problems with second-order cone constraints, which give rise to nonlinear complementarity problems, with applications to stochastic equilibrium problems in water infrastructure and finance. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Funding: This work was supported by the Division of Civil, Mechanical and Manufacturing Innovation [Grant 2113891], the Division of Electrical, Communications and Cyber Systems [Grant 2114100], the Office of Naval Research Global [Grant 00014-22-1-2649], Petrobras [Grant 4324713], the Division of Mathematical Sciences [Grant 2318519], and the Danmarks Frie Forskningsfond [Grant 0217-00009B]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0822 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0822 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Mobile technologies and AI enable health data collection from devices, allowing effective monitoring. Traditional methods often compromise privacy, but federated learning (FL) offers a potential solution. However, current FL approaches face two issues: they don’t identify key health features for clinical intervention, and their aggregation overlooks patient multidimensional heterogeneity. This study seeks to develop a new FL method to tackle these challenges and enhance privacy in mobile health monitoring. This study proposes a novel FL method combining (1) a spatial and temporal attention-based prediction model (STA-Pred) that uses attention to identify key spatial and temporal features, and (2) a multidimensional heterogeneity-based aggregation protocol (MDH-Aggr), which aggregates components based on their heterogeneity to handle multidimensional differences. Experiments on three data sets show that our method outperforms existing methods in several patient-monitoring contexts. This study enhances understanding of how to leverage mobile technologies and AI to enable privacy-preserving health monitoring that promotes the social good. Additionally, it advances FL research through two innovative designs (STA-Pred and MDH-Aggr). History: This paper has been accepted by Kaushik Dutta for the Special Issue on Responsible AI and Data Science for Social Good. Funding: Y. Chai, H. Liu, and Y. Liu are supported by the National Natural Science Foundation of China [Grants 72342011, 72322019, 72188101, and 72402001]. Dr. L. Wang’s work was in part supported by the National Natural Science Foundation of China [Grant 72271027], Hainan Provincial Natural Science Foundation of China [Grant 726MS0458], and Beijing Institute of Technology Research Fund Program for Young Scholars [Grant XSQD-202216004]. X. Liu is not supported by any funds or associated with any of the abovementioned funds. Supplemental Materials: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0521 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0521 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/.

Infodemic is a significant concern for society, and numerous studies have explored artificial intelligence (AI)-based solutions to manage it. However, existing methods fail to make sense of the complex situation of infodemic and fail to manage infodemic in a proactive manner. To tackle this challenge, we formalize infodemic topic prediction (ITP) as a distinct research problem. ITP aims to identify emerging topics to provide a global view of the situation, as well as to predict the probability of a topic becoming an infodemic to enable proactive management. Although some relevant methods exist, they are insufficient in modeling the intertopic relationships where each topic is closely connected to all others in a group of topics (called multilateral intertopic relationships). This study proposes a new hypergraph-based ITP framework that models the multilateral intertopic relationship with a novel hypergraph. Specifically, we introduce a novel temporal topic hypergraph (TTH) where topics are treated as nodes, and the multilateral intertopic relationships in both semantic and temporal perspectives are modeled by hyperedges. The main novelties are twofold. First, our TTH is a novel hypergraph that can cope with newly emerged topics through the proposed directed and undirected hyperedges. Second, we propose a novel similarity-based transformation method (STM) that reduces the complexity of hypergraph transformation from [Formula: see text] to [Formula: see text], making it scalable for social media data. Evaluations on infodemics during the COVID-19 and the Mpox pandemic demonstrate the effectiveness of our framework. This study contributes to responsible infodemic management by formalizing the task of ITP and introducing a novel framework for it, which enables public health organizations, social media platforms, and policymakers to proactively make sense of and effectively respond to infodemic. History: This paper has been accepted by Kaushik Dutta for the Special Issue on the Responsible AI and Data Science for Social Good. Funding: L. Zhao, S. Ding, Y. Chai and S. Yang are supported by the National Natural Science Foundation of China [Grants 72293581, 72188101, 72342011, and 72322019]. J. Xie and X. Fang are not supported by any funds and are not associated with any of the above mentioned funds. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0660 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0660 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

There is a recent proliferation of research on the integration of machine learning and optimization. One expansive area within this research stream is optimization over pre-trained predictive models, which proposes the use of pre-trained predictive models as surrogates for uncertain or highly complex objective functions. In this setting, features of the predictive models become decision variables in the optimization problem. Despite a recent surge in publications in this area, only a few papers note the importance of incorporating trust-region considerations in this decision-making pipeline, that is, enforcing solutions to be similar to the data used to train the predictive models. Without such constraints, the evaluation of the predictive model at solutions obtained from optimization cannot be trusted and the practicality of the solutions may be unreasonable. In this paper, we provide an overview of the approaches appearing in the literature to construct a trust region and propose three alternative approaches. Our numerical evaluation highlights that trust-region constraints learned through our newly proposed approaches compare favorably with previously suggested approaches, both in terms of solution quality and computational time. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0312 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0312 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Kidney exchange problems with immunosuppressants extend the classical kidney exchange problem by allowing a limited number of arcs that do not belong to the compatibility graph to be included in the solution. Among these, the kidney exchange problem with reserve arcs (KEP-RA) assumes that any arc can be made compatible, whereas the kidney exchange problem with half-compatible arcs (KEP-HCA) assumes that this is the case only for a subset of arcs. Starting with KEP-RA, we first show that existing integer linear programming formulations for the kidney exchange problem can easily be extended to include reserve arcs. We then demonstrate that there always exists an optimal KEP-RA solution in which every cyclic set of exchanges contains at most one reserve arc, and we use this property to develop effective variable reduction procedures and new ad hoc modeling structures. We also extend these findings to the case where nondirected donors are included. Experiments show that trivial model extensions are not able to cope with medium-sized instances, whereas our enhanced models can solve instances with up to a thousand recipient-donor pairs. We also evaluate the number of transplants enabled by each reserve arc in various settings, demonstrating that although reserve arcs tend to have a diminishing return, there are instances for which this is not the case. Finally, we demonstrate how our techniques can be integrated into a variable and constraint generation algorithm to solve KEP-HCA and show that its performance complements that of a trivial model extension. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: D. Manlove was supported by the Engineering and Physical Sciences Research Council [Grant EP/X013618/1]. This work used the Dutch national e-infrastructure with the support of the SURF Cooperative [Grants EINF-8220 and EINF-12272]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.1071 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.1071 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .