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A temporal XML database could become an inconsistent model of the represented reality after a retroactive update. Such an inconsistency state must be repaired by performing corrective actions (e.g. payment of arrears after a retroactive salary increase) either immediately (i.e. at inconsistency detection time) or in a deferred manner, at one or several chosen repair times according to application requirements. The purpose of this work is to deal with deferred and multi-step repair of detected data inconsistencies.

A general approach for deferred and stepwise repair of inconsistencies that result from retroactive updates of currency data (e.g. the salary of an employee) in a valid-time or bitemporal XML database is proposed. The approach separates the inconsistency repairs from the inconsistency detection phase and deals with the execution of corrective actions, which also take into account enterprise’s business rules that define some relationships between data.

Algorithms, methods and support data structures for deferred and multi-step inconsistency repair of currency data are presented. The feasibility of the approach has been shown through the development and testing of a system prototype, named Deferred-Repair Manager.

The proposed approach implements a new general and flexible strategy for repairing detected inconsistencies in a deferred manner and possibly in multiple steps, according to varying user’s requirements and to specifications which are customary in the real world.

Data integration is to combine data residing at different sources and to provide the users with a unified interface of these data. An important issue on data integration is the existence of conflicts among the different data sources. Data sources may conflict with each other at data level, which is defined as data inconsistency. The purpose of this paper is to aim at this problem and propose a solution for data inconsistency in data integration.

A relational data model extended with data source quality criteria is first defined. Then based on the proposed data model, a data inconsistency solution strategy is provided. To accomplish the strategy, fuzzy multi-attribute decision-making (MADM) approach based on data source quality criteria is applied to obtain the results. Finally, users feedbacks strategies are proposed to optimize the result of fuzzy MADM approach as the final data inconsistent solution.

To evaluate the proposed method, the data obtained from the sensors are extracted. Some experiments are designed and performed to explain the effectiveness of the proposed strategy. The results substantiate that the solution has a better performance than the other methods on correctness, time cost and stability indicators.

Since the inconsistent data collected from the sensors are pervasive, the proposed method can solve this problem and correct the wrong choice to some extent.

In this paper, for the first time the authors study the effect of users feedbacks on integration results aiming at the inconsistent data.

Grounded on the technology-organization-environment (TOE) framework, the purpose of this paper is to develop a two-stage model of radio frequency identification (RFID) adoption in livestock businesses. RFID adoption is divided into two stages, acceptance and extension. It is argued that RFID adoption in livestock businesses is influenced by technological (interoperability, technology readiness), organizational (readiness, market scope), and environmental (competitive market pressure, data inconsistency) factors.

From a qualitative field study, along with the support of existing literature, the authors developed a research model, which was then validated with survey data of 318 livestock businesses in Australia. Data analysis used partial least squares structural equation modeling.

Empirical results showed that interoperability, organizational readiness, and competitive market pressure, and data inconsistency significantly influence acceptance of RFID technology in livestock businesses. In addition, the extended use of RFID is determined mainly by interoperability, technology readiness, organizational market scope, and data inconsistency. The results suggested differential effect of data inconsistency– it had a negative influence on RFID acceptance but a positive impact on the extent of its use.

This is one of the first studies to examine RFID adoption as a two-stage process. The theoretical basis was based on TOE framework and the factors were developed from a field study. The results of this study will provide insights for different livestock industry including technologists, farm managers, and market players.

The historical data usually consist of overlapping reports, and these reports may contain inconsistent data, which may return incorrect results of a query search. Moreover, users who issue the query may not learn of this inconsistency even after a data cleaning process (e.g. schema matching or data screening). The inconsistency can exist in different types of data, such as temporal or spatial data. Therefore, this paper aims to introduce an information fusion method that can detect data inconsistency in the early stages of data fusion.

This paper introduces an information fusion method for multi-robot operations, for which fusion is conducted continuously. When the environment is explored by multiple robots, the robot logs can provide more information about the number and coordination of targets or victims. The information fusion method proposed in this paper generates an underdetermined linear system of overlapping spatial reports and estimates the case values. Then, the least squares method is used for the underdetermined linear system. By using these two methods, the conflicts between reports can be detected and the values of the intervals at specific times or locations can be estimated.

The proposed information fusion method was tested for inconsistency detection and target projection of spatial fusion in sensor networks. The proposed approach examined the values of sensor data from simulation that robots perform search tasks. This system can be expanded to data warehouses with heterogeneous data sources to achieve completeness, robustness and conciseness.

Little research has been devoted to the linear systems for information fusion of tasks of mobile robots. The proposed information fusion method minimizes the cost of time and comparison for data fusion and also minimizes the probability of errors from incorrect results.

This paper seeks to investigate the role of information systems integration in Irish small and medium‐sized enterprises operating in the manufacturing sector.

Research was conducted through a review of literature and subsequent primary research involving qualitative (semi‐structured interviews) and quantitative (questionnaires) research strategies.

The paper reveals the sophistication of internal IT infrastructure within Irish manufacturing SMEs and whether the IT systems are integrated with one another, and, if so, how well that integration is done. Moreover, the owner‐managers' and senior managers' attitude to IS integration issues is explored, including the reasons that prompt them to integrate IT systems within their businesses, their expectations of IS integration, the challenges they recognise when integrating the systems and their criteria regarding IS integration.

The research focuses on manufacturing SMEs operating in Ireland; other sectors are not investigated.

The paper helps the owner‐managers and senior managers to understand the issues of IS integration and points towards possible solutions to the problem of disparate IT systems.

The negative impact of disparate systems and the benefits of integrating them in an SMEs environment have not been thoroughly examined to date.

The impact of a mixture of positive and negative media coverage on long-run hotel survival remains unknown. This paper aims to investigate how the mixed positive and negative media coverage, namely, inconsistent media coverage, influences long-run hotel survival.

A yearly panel data set covering 792 news-reported hotels in Guangdong province of China, over the period 2010–2020, is analyzed using an inconsistency analysis framework consisting of text mining and survival analysis. The estimates of exponential models on the same observations and Cox estimates on alternative observations are used for robustness checks.

The inconsistency calculation method proposed here can measure the controversy degree well. There exists a U-shaped relationship between inconsistency of media coverage and hotel longevity, and hotel survival is significantly reduced only when the degree of inconsistency is within the range of 17.8%–53.6%. The U-shaped relationship is moderated by negative hotel image and by online media coverage on hotel operation strategy topics.

This study provides suggestions for hotel managers to use media coverage inconsistency to increase long-run hotel survival in the digital era.

To the best of the authors’ knowledge, this paper is one of the first to investigate long-run hotel survival factors from the perspective of media coverage inconsistency. It also proposes a method to calculate the degree of media coverage controversy, which helps to quantify the relationship between the degree of inconsistency and hotel survival.

Linking Open Data (LOD) provides a vast amount of well structured semantic information, but many inconsistencies may occur, especially if the data are generated with the help of automated methods. Data cleansing approaches enable detection of inconsistencies and overhauling of affected data sets, but they are difficult to apply automatically. The purpose of this paper is to present WhoKnows?, an online quiz that generates different kinds of questionnaires from DBpedia data sets.

Besides its playfulness, WhoKnows? has been developed for the evaluation of property relevance ranking heuristics on DBpedia data, with the convenient side effect of detecting inconsistencies and doubtful facts.

The original purpose for developing WhoKnows? was to evaluate heuristics to rank LOD properties and thus, obtain a semantic relatedness between entities according to the properties by which they are linked. The presented approach is an efficient method to detect popular properties within a limited amount of triples. Ongoing work continues in the development of sound property ranking heuristics for the purpose of detecting the most relevant characteristics of entities.

WhoKnows? uses the approach of “Games with a Purpose” to detect inconsistencies in Linked Data and score properties to rank them for sophisticated semantic search scenarios.

Reference data collection tools facilitate the collection of in‐depth data about reference interactions. Since this information may influence decisions, library managers should examine how these tools are used and assess how these data entry behaviors may impact the accuracy of the data. This paper aims to analyze reference staff perceptions and data entry behaviors using a reference data collection tool.

The two‐year mixed method study analyses reference staff perceptions and data entry behaviors related to the reference data collection tool used at the University of Colorado Boulder Libraries. The authors identified six distinct data entry behaviors for analysis in this study.

The survey results indicate that staff consider the tool to be both easy to use and useful. These findings, under the technology acceptance model, indicate technology acceptance, which influences adoption and use of the tool. Though rates of adoption and use of the tool are high, the authors' analysis of behaviors indicate that not all users record reference interactions in the same way, and this inconsistency may impact the accuracy of collected data.

Inconsistency in data entry behaviors should inform the design of staff training sessions, best practice guidelines, and the tool's interface.

If data are used to justify changes to services and collections, decision makers need to be confident that data accurately reflect activity at library service points.

Previous studies related to reference data collection mention the importance of consistent data entry practices, but no studies have explicitly evaluated how inconsistencies in use may impact the accuracy of data.

This paper aims to investigate data elements, transfer, gaps and the challenges to implement data analytics in facilities management. The goal is not to search for a definite solution but to gather necessary information, understand the challenges faced and develop a proper foundation for future study.

This paper used a case study approach with a qualitative method. The case of the Georgia Institute of Technology was investigated by having a semi-structured interview with six relevant personnel. The recorded interview content was analyzed and presented based on six work processes.

Higher education institutions are taking initiatives but facing challenges in implementing data analytics. There were 36 software tools used to manage different aspects of facilities at Georgia Tech. Identified data elements and data processing indicated that major challenges for data-driven decision-making were inconsistency in data input and structure, the issue of interoperability among different software tools and a lack of software training.

The authors only interviewed individuals who work closely with data gathering, transfer and processing. Thus, the study did not explore the perspective of individuals in the leadership level or the user group level.

Facilities management departments in higher education institutions perform multi-disciplinary functions, including building automation, continuous commissioning and preventative maintenance, all of which are data- and technology-intensive. Managing this overwhelming amount of information is often a challenge, but well-planned data analytics can be used to draw keen insights about any aspect of facilities management and operations and assist in evidence-based decision-making.

The aim of this paper was to study current practices in FM work order processing to support and improve decision-making. Processing and prioritizing work orders constitute a critical part of facilities and maintenance management practices given the large amount of work orders submitted daily. User-driven approaches (UDAs) are currently more prevalent for processing and prioritizing work orders but have challenges including inconsistency and subjectivity. Data-driven approaches can provide an advantage over user-driven ones in work-order processing; however, specific data requirements need to be identified to collect and process the functional data needed while achieving more consistent and accurate results.

This paper presents the findings of an online survey conducted with facility management (FM) experts who are directly or indirectly involved in processing work orders in building maintenance.

The findings reflect the current practices of 71 survey participants on data requirements, criteria selection, rankings, with current shortcomings and challenges in prioritizing work orders. In addition, differences between criteria and their ranking within participants’ experience, facility types and facility sizes are investigated. The findings of the study provide a snapshot of the current practices in FM work order processing, which aids in developing a comprehensive framework to support data-driven decision-making and address the challenges with UDAs.

Although previous studies have explored the use of selected criteria for processing and prioritizing work orders, this paper investigated a comprehensive list of criteria used by various facilities for processing work orders. Furthermore, previous studies are focused on the processing and prioritization stage, whereas this paper explored the data collected following the completion of the maintenance tasks and the benefits it can provide for processing future work orders. In addition, previous studies have focused on one specific stage of work order processing, whereas this paper investigated the common data between different stages of work order processing for enhanced FM.