Qualitative effects of knowledge rules and user feedback in probabilistic data integration

User feedback is vital to a radical new product development (NPD) effort. However, shared team values (beliefs and possibly ideology) determine whether a development team will act on user feedback. This paper addresses this important issue by investigating the interaction between internal team values and external market feedback or information in radical projects. The underlying logic in investigating NPD team values is that, although user feedback is vital to a radical NPD effort, shared team values may well influence whether a development team will act on user feedback regardless of the team's competences in collecting, integrating, and assimilating the information acquired. Previous research has considered how exploratory market learning processes moderate market and technological uncertainty in radical product development. Scholars argue that NPD teams may increase the chances of success of radically new projects by acquiring, assimilating, and implementing new information from market feedback. They assume that managers and NPD teams' ability to make sound choices along the NPD process and increase learning depends on quality and quantity of information acquired from user feedback. The more and better information acquired, the easier it will be for the team to assimilate and implement it with clear benefits for the NPD project. Therefore, extant research has led to a deeper understanding of what information is important for exploratory market learning. It has not tackled, however, how information is assimilated by the NPD team and to what extent the information from the market is implemented. Given the exploratory nature of the research objective, a longitudinal participant–observation case study research is adopted, in which during a two‐year period a radical project development in the mobility field was investigated. During this period, the NPD team adopted various and sophisticated market research techniques to acquire user feedback from the design to the prototype and test phase. These units of information were collected, and the extent to which shared team values influence the process through which user feedback was assimilated, implemented, and used was analyzed. The findings suggest that information assimilation is not automatic but rather influenced in interesting ways by internal team values. The findings imply that shared team values act as a selective assimilation mechanism determining whether a development team will act on user feedback. Furthermore, the type of information (e.g., functional vs. conceptual feedback) processed by the development team acts as a moderating factor on the relationship between the team values and information processing. In conclusion, this paper highlights how one of the mechanisms for information selection is based on team values and the type of information presented to the team. The topic of selective assimilation and use in exploratory market learning is an important topic worthy of further research. It is important for academic researchers because it provides a better description of how radical product development projects evolve. It is important for managers because awareness of biases and the existence of selective assimilation may lead to better product development outcomes.

Data interoperability is a major issue in data management for data science and big data analytics. Probabilistic data integration (PDI) is a specific kind of data integration where extraction and integration problems such as inconsistency and uncertainty are handled by means of a probabilistic data representation. This allows a data integration process with two phases: (1) a quick partial integration where data quality problems are represented as uncertainty in the resulting integrated data, and (2) using the uncertain data and continuously improving its quality as more evidence is gathered. The main contribution of this paper is an iterative approach for incorporating evidence of users in the probabilistically integrated data. Evidence can be specified as hard or soft rules (i.e., rules that are uncertain themselves).

Scheduling and staffing are important management activities in software projects. In closed-source software development, the relationships among development effort, time, and staffing have been well established and validated: the development effort determines the development time and the best number of developers that should be allocated to the project. However, there has been no similar research reported in open-source projects. In this chapter, the authors study the development effort, development time, and staffing in an open-source project, the Linux kernel project. Specifically, they investigate the power law relations among development effort, development time, and the number of active developers in the Linux kernel project. The authors find the power law relations differ from one branch to another branch in the Linux kernel project, which suggests different kinds of management and development styles might exist in different branches of the Linux kernel project. The empirical knowledge of software development effort obtained in this study could help project management and cost control in both open-source communities and closed-source industries.

The advent of the information age has revolutionized data collection and has led to a rapid expansion of available data sources. Methods of data integration are indispensable when a question of interest cannot be addressed using a single data source. Record linkage (RL) is at the forefront of such data integration efforts. Incentives for sharing linked data for secondary analysis have prompted the need for methodology accounting for possible errors at the RL stage. Mismatch error is a common consequence resulting from the use of nonunique or noisy identifiers at that stage. In this paper, we present a framework to enable valid postlinkage inference in the secondary analysis setting in which only the linked file is given. The proposed framework covers a variety of statistical models and can flexibly incorporate information about the underlying RL process. We propose a mixture model for linked records whose two components reflect distributions conditional on match status, i.e. correct or false match. Regarding inference, we develop a method based on composite likelihood and the expectation-maximization algorithm that is implemented in the R package pldamixture. Extensive simulations and case studies involving contemporary RL applications corroborate the effectiveness of our framework.

SmokePath Explorer is a web-based decision-support tool for California, U.S.A. that quantifies smoke transport probability and population exposure risk across the state, enabling data-driven strategies to minimize impacts while advancing fire management effectiveness. SmokePath integrates the California and Nevada Smoke and Air Committee (CANSAC) high-resolution (2-km) 20-year reanalysis climatology with HYSPLIT trajectory modeling. A total of 1.3 billion transport trajectories were precomputed with initializations four times per day at four distinct height levels capturing diurnal variations and injection height influences across various prescribed fire scenarios to support probabilistic smoke projections. Within the SmokePath online dashboard, users can input fire parameters and select specific months or weeks to assess smoke transport risk. The tool generates risk-level smoke transport contours from precomputed data and summarizes key meteorological variables. The system also provides population exposure estimates, including the total affected population, the number of smoke-sensitive facilities (i.e. educational and healthcare), and impacted USPS ZIP codes. User feedback has been key to developing SmokePath, enhancing usability, data integration, and decision-making for prescribed fire planning. To assess SmokePath’s efficacy, we conducted case studies across diverse regions and validated results against independent observation datasets. These use cases assessed the accuracy of the WRF-CANSAC reanalysis dataset, which serves as the meteorological input for fire weather climatology and trajectory modeling. The fire case studies focused on identifying optimal burn windows and quantifying smoke transport patterns from (i) large multi-day prescribed fires, (ii) short duration (single-day) pile burns, and (iii) burns across diverse regions with complex topographic features. In most cases, modeled transport probability aligned with satellite-observed smoke plumes, capturing predominant dispersion patterns. Stakeholder feedback further supported the tool’s practical utility — 85% indicated they would use SmokePath for prescribed fire planning, and 62% found it useful during wildfires. Implications: With catastrophic wildfires on the rise, California is expanding fuel treatments, including prescribed fire. While essential for mitigation, prescribed burns release smoke that can harm public health if unmanaged. SmokePath addresses this challenge by providing evidence-based insights on plume behavior to guide short- and long-term planning. The tool helps land managers schedule burns to minimize community impact, especially for vulnerable populations, and supports wildfire response with rapid smoke risk information. By improving public communication and enabling protective actions, SmokePath advances both health protection and the strategic use of prescribed fire.

Discovering and Reconciling Value Conflicts for Data Integration

The development of the marketplace produces a variety of features to attract users to engage with content. Gamification is one of the marketing tools that is currently trending and many are already using it, one of which is the marketplace. The purpose of this study is to examine the impact of the application of gamification in the marketplace on the effect of marketplace user feedback. The marketplaces used in this research are Shopee and Tokopedia. This study uses a quantitative method using the Self Determination Theory model combined with the Technology Acceptance Model. The results of the questionnaire data collection from 252 data contained 208 valid data. The results of data processing using SmartPLS produced four indicators with values ​​below 0.7 including A1, A5, PEU4, and C5, so modifications were made to the research model. Based on the results of data processing, all of the hypotheses of this study were significant or accepted as many as 9 hypotheses. The results show that user attitudes greatly determine the sustainability of users towards the use of the marketplace and the pleasure of users when playing games on the marketplace is one of the main factors in determining user attitudes towards the marketplace. This study provides recommendations that related parties in making game features on the marketplace focus more on user enjoyment when playing games, because user pleasure is one of the main factors in determining user attitudes and user attitudes towards game features on the marketplace encourage users' intentions to use the marketplace.

With the progression of climate change, Central Europe faces an increasing risk of extreme weather events, particularly floods and wildfires. One of the latest events took place in June 2024, when continuous rainfall in southern Germany caused water levels to rise. This paper presents the German Aerospace Center’s (DLR) operational response to the flood incident, focusing on the integration of Earth observation data and the dissemination of information via a web-based platform. DLR combined high-resolution aerial imagery, very high-resolution radar and optical satellite imagery, and utilized flood extent masks derived from Copernicus Sentinel-1 and -2 data to support the flood response. The collected datasets were refined with ancillary geospatial layers such as building footprints and infrastructure data, before being integrated into a web-based viewer. The viewer allowed a dynamic visualization and data exploration by public authorities and disaster response organizations, facilitating efficient coordination and decision-making. The first information layers were published within hours of acquisition, and the web-based viewer was accessed more than 800 times during the first week. User feedback from emergency stakeholders highlighted the platform’s usability, relevance, and value in crisis coordination. While certain limitations were identified such as the reliance on internet access and challenges in urban radar interpretation, the workflow proved highly effective. This case study demonstrates how a national EO agency can leverage integrated geospatial data, automation, and dissemination tools to support rapid flood response. The approach presented in this paper provides a framework that can potentially be replicated in other countries and disaster scenarios.

Research laboratories produce a huge amount of complex and heterogeneous data typically managed by Laboratory Information Management Systems (LIMSs). Although many LIMSs are available, it is often difficult to identify a product that covers all the requirements and peculiarities of a specific institution. To deal with this lack, the Candido Cancer Institute decided to start a project, named the Laboratory Assistant Suite (LAS), with the aim of developing a new software platform that assists researchers throughout diverse laboratory activities. The proposed system can track laboratory experiments even in problematic environments, support the integration of heterogeneous biomedical data, and help in decision-making tasks. In this chapter, the authors present the current architecture of the system, some real-use cases, as well as statistics about stored data and user feedback in order to provide an overview of the functionalities and show the effectiveness of the platform in supporting research in the molecular oncology field.

Fake news is a major threat to global democracy resulting in diminished trust in government, journalism and civil society. The public popularity of social media and social networks has caused a contagion of fake news where conspiracy theories, disinformation and extreme views flourish. Detection and mitigation of fake news is one of the fundamental problems of our times and has attracted widespread attention. While fact checking websites such as snopes, politifact and major companies such as Google, Facebook, and Twitter have taken preliminary steps towards addressing fake news, much more remains to be done. As an interdisciplinary topic, various facets of fake news have been studied by communities as diverse as machine learning, databases, journalism, political science and many more. The objective of this tutorial is two-fold. First, we wish to familiarize the database community with the efforts by other communities on combating fake news. We provide a panoramic view of the state-of-the-art of research on various aspects including detection, propagation, mitigation, and intervention of fake news. Next, we provide a concise and intuitive summary of prior research by the database community and discuss how it could be used to counteract fake news. The tutorial covers research from areas such as data integration, truth discovery and fusion, probabilistic databases, knowledge graphs and crowdsourcing from the lens of fake news. Effective tools for addressing fake news could only be built by leveraging the synergistic relationship between database and other research communities. We hope that our tutorial provides an impetus towards such synthesis of ideas and the creation of new ones.

The information managed in emerging applications, such as sensor networks, location-based services, and data integration, is inherently imprecise. To handle data uncertainty, probabilistic databases have been recently developed. In this paper, we study how to quantify the ambiguity of answers returned by a probabilistic top-k query. We develop efficient algorithms to compute the quality of this query under the possible world semantics. We further address the cleaning of a probabilistic database, in order to improve top-k query quality. Cleaning involves the reduction of ambiguity associated with the database entities. For example, the uncertainty of a temperature value acquired from a sensor can be reduced, or cleaned, by requesting its newest value from the sensor. While this 'cleaning operation' may produce a better query result, it may involve a cost and fail. We investigate the problem of selecting entities to be cleaned under a limited budget. Particularly, we propose an optimal solution and several heuristics. Experiments show that the greedy algorithm is efficient and close to optimal. © 2013 IEEE.

<p>Data annotation is the categorization and labelling of data for applications, such as machine learning, artificial intelligence, and data integration. The categorization and labelling are done to achieve a specific use case in relation to solving problems. Existing data annotation systems and modules face imperfections such as knowledge and annotation not being formally integrated, narrow application range, and difficulty to apply on existing database management applications. To analyze and process annotated data, obtain the relationship between different annotations, and capture metainformation in data provenance and probabilistic databases, in this paper, we design a back-end query processing framework as a supplementary interface for the database management system to extend operation to datasets and boost efficiency. The framework utilizes Java language and the MVC model for development to achieve lightweight, cross-platform, and high adaptability identities. The contribution of this paper is mainly reflected in two aspects. The first contribution is to implement query processing, provenance semiring, and semiring homomorphism over annotated data. The second contribution is to combine query processing and provenance with SQL statements in order to enable the database manager to invoke operations to annotation.</p> <p> </p>

To evaluate progress on political biodiversity objectives, biodiversity monitoring provides information on whether intended results are being achieved. Despite scientific proof that monitoring and evaluation increase the (cost) efficiency of policy measures, cost estimates for monitoring schemes are seldom available, hampering their inclusion in policy programme budgets. Empirical data collected from 12 case studies across Europe were used in a power analysis to estimate the number of farms that would need to be sampled per major farm type to detect changes in species richness over time for four taxa (vascular plants, earthworms, spiders and bees). A sampling design was developed to allocate spatially, across Europe, the farms that should be sampled. Cost estimates are provided for nine monitoring scenarios with differing robustness for detecting temporal changes in species numbers. These cost estimates are compared with the Common Agricultural Policy (CAP) budget (2014–2020) to determine the budget allocation required for the proposed farmland biodiversity monitoring. Results show that the bee indicator requires the highest number of farms to be sampled and the vascular plant indicator the lowest. The costs for the nine farmland biodiversity monitoring scenarios corresponded to 0·01%–0·74% of the total CAP budget and to 0·04%–2·48% of the CAP budget specifically allocated to environmental targets. Synthesis and applications. The results of the cost scenarios demonstrate that, based on the taxa and methods used in this study, a Europe-wide farmland biodiversity monitoring scheme would require a modest share of the Common Agricultural Policy budget. The monitoring scenarios are flexible and can be adapted or complemented with alternate data collection options (e.g. at national scale or voluntary efforts), data mobilization, data integration or modelling efforts.

3603 Background: The integration of multi-omics and clinical data in precision medicine research for colorectal cancer (CRC) is a complex task that requires advanced computational tools. Artificial Intelligence agent for High-Optimization and Precision mEdicine (AI-HOPE) has emerged as a transformative platform, streamlining data integration, analysis, and discovery efforts. AI-HOPE is designed to integrate and analyze multi-omics alongside clinical data, facilitating novel insights into CRC pathogenesis, therapeutic responses, and precision medicine applications. Methods: AI-HOPE leverages Large Language Models (LLMs) to interpret natural language inputs and convert them into executable pipelines for multi-omics and clinical data analysis. Data from The Cancer Genome Atlas (TCGA) and other publicly available CRC datasets were utilized to demonstrate its capabilities. AI-HOPE supports analyses such as identifying mutation and gene expression patterns, pathway enrichment, survival analysis, and therapeutic outcome predictions. Three case studies were conducted: (1) identifying WNT and TGFβ pathway alterations in early-onset CRC (EO CRC) versus late-onset CRC, (2) evaluating the association between specific multi-omics signatures and progression-free survival in patients treated with FOLFOX chemotherapy, and (3) performing a precision medicine query to identify actionable gene mutations and tailored medications for CRC treatment. Results: Overall AI-HOPE answered queries with an accuracy of 0.98 and an F1 score of 0.89 (precision = 0.80). AI-HOPE identified significant alterations in the WNT and TGFβ pathways among EO CRC patients compared to late-onset cases, aligning with findings from published literature. In the second study, the platform revealed that patients with specific transcriptomic signatures (e.g., upregulation of MYC targets) had significantly worse progression-free survival when treated with FOLFOX chemotherapy, further supporting its utility in identifying clinically relevant biomarkers. AI-HOPE was used to query CRC datasets for actionable gene mutations, such as KRAS, BRAF, and MSI-H (microsatellite instability-high), and cross-referenced these findings with drug databases to identify tailored therapies. The analysis highlighted FDA-approved targeted treatments, such as EGFR inhibitors (cetuximab and panitumumab) for KRAS wild-type patients and immune checkpoint inhibitors (pembrolizumab and nivolumab) for MSI-H tumors. AI-HOPE also identified emerging therapeutic options from ongoing clinical trials, showcasing its potential for guiding precision medicine strategies in CRC. Conclusions: This study demonstrates the transformative potential of AI-HOPE in advancing CRC precision medicine research by seamlessly integrating multi-omics and clinical data.

Background: Patient safety dashboards are increasingly used to monitor and improve patient safety in healthcare. However, their effective development faces significant challenges. This qualitative study explores these challenges and identifies potential solutions. Methods: A phenomenological approach was used to investigate the experiences of healthcare managers, patient safety officers, quality improvement specialists, and information technology professionals involved in patient safety dashboard development within healthcare facilities of Tabriz University of Medical Sciences in Tabriz, Iran. Participants were selected via purposive sampling. Inclusion criteria were direct experience with patient safety dashboard development within the past 5 years; exclusion criteria included no direct involvement or roles unrelated to patient safety. Data were collected through semi-structured interviews and analyzed using thematic analysis to identify challenges and solutions. Results: A total of 20 individuals participated in this study. The study identified two main themes: Challenges and solutions in developing patient safety dashboards. Within these themes, five sub-themes related to challenges were identified: Data integration complexities, disparate data sources, lack of standardized metrics, resource constraints, and varying levels of digital maturity. Similarly, five sub-themes related to solutions were identified: Interoperability solutions, standardized metrics development, resource allocation, capacity building initiatives, and digital transformation strategies. A total of 72 individual codes were generated from the data, representing specific examples of challenges and solutions. Conclusions: Addressing challenges and providing solutions for developing patient safety dashboards is vital for enhancing patient safety practices and driving quality improvement initiatives toward delivering high-quality, safe care to patients. By prioritizing data integration, standardized metrics, resource allocation, capacity building, and digital transformation efforts, organizations can develop dashboards to strengthen patient safety practices and continuously improve patient care.