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The potential of focused beam reflectance measurement (FBRM) and electrical resistance tomography (ERT) was evaluated as on‐line process analytical technologies (PAT) for monitoring powder rehydration. Skim milk powder (SMP) and stage one infant formulas were evaluated. Among all samples, SMP had the greatest conductivity by ERT (P < 0.05), this was confirmed by off‐line methods, that is, SMP had a higher solubility, whereas infant formulas had lower solubility. Particle counts for fines of all samples reached peak values ~450 s. Results demonstrated PAT tools offer capability for rehydration characterisation of dairy and non‐dairy powders. [ABSTRACT FROM AUTHOR]

The editorial in the International Journal of Dairy Technology highlights recent studies by Chinese researchers on testing dairy products for safety, new product development, and future industry trends in China. Starting in January 2025, the journal will introduce structured abstracts and continuous publication, replacing the Early View workflow. Free format submissions will also be offered to simplify the submission process. Additionally, the Editor's Choice Article for November 2024 discusses the rehydration characteristics of dairy and infant formula powders, emphasizing the importance of monitoring powder rehydration for product quality. [Extracted from the article]

Accounting for about 290,000–650,000 deaths across the globe, seasonal influenza is estimated by the World Health Organization to be a major cause of mortality. Hence, there is a need for a reliable and robust epidemiological surveillance decision‐making system to understand and combat this epidemic disease. In a previous study, the authors proposed a decision support system to fight against seasonal influenza. This system is composed of three subsystems: (i) modeling and simulation, (ii) data warehousing, and (iii) analysis. The analysis subsystem relies on spatial online analytical processing (S‐OLAP) technology. Although the S‐OLAP technology is useful in analyzing multidimensional spatial data sets, it cannot take into account the inherent multicriteria nature of seasonal influenza risk assessment by itself. Therefore, the objective of this article is to extend the existing decision support system by adding advanced multicriteria analysis capabilities for enhanced seasonal influenza risk assessment and monitoring. Bearing in mind the characteristics of the decision problem considered in this article, a well‐known multicriteria classification method, the dominance‐based rough set approach (DRSA), was selected to boost the existing decision support system. Combining the S‐OLAP technology and the multicriteria classification method DRSA in the same decision support system will largely improve and extend the scope of analysis capabilities. The extended decision support system has been validated by its application to assess seasonal influenza risk in the northwest region of Algeria. [ABSTRACT FROM AUTHOR]

Summary: The advent of big data technologies has changed the way many companies manage their data. Several companies moved their data to the cloud using the concept of database‐as‐a‐service (DBaaS). Moving databases to the cloud presents several challenges related to flexible and scalable management of data. Although some of these companies migrated to NoSQL databases, most still rely on relational databases in the cloud to manage data, especially data that is critical to the decision making process. Online analytical processing (OLAP) queries take a long time to be processed, thus demanding high‐performance capabilities from their associated database systems to get results in a feasible time. In this article, we propose a middleware solution that can be deployed in any cloud provider, named C‐ParGRES, which explores database replication and interquery and intraquery parallelism to efficiently support OLAP queries in the cloud. C‐ParGRES is an extension of ParGRES, an open‐source database cluster middleware for high‐performance OLAP query processing in clusters. C‐ParGRES exploits cloud capabilities such as on‐demand resource provisioning and elasticity. In addition, C‐ParGRES can create multiple and independent virtual clusters for different database and users. We evaluate C‐ParGRES with two real‐world OLAP applications, both from the Brazilian Institute of Geography and Statistics. Results show that C‐ParGRES is a cost‐effective solution for OLAP query processing in the cloud. [ABSTRACT FROM AUTHOR]

Natural cotton fiber was applied as a green extraction material for in‐tube solid‐phase microextraction. Cotton fibers were characterized by scanning electron microscope. A bundle of cotton fibers (685 mg, 20 cm) was directly packed into a polyetheretherketone tube (i.d. 0.75 mm) to get the extraction device. It was connected into high performance liquid chromatography, building an online extraction and dectection system. Through the online analysis system, several polycyclic aromatic hydrocarbons were used as the targets to evaluate the extraction performace of the device. In order to get high extraction efficiency and sensitivity, the extraction and desorption conditions were optimized. Under the optimum conditions, the sensitive analysis method was established, and provided low limits of detection of 0.02 and 0.05 μg/L, good linearity ranges of 0.06–15 and 0.16–15 μg/L, as well as high enrichment factors of 176–1868. The method was applied to the online determination of trace polycyclic aromatic hydrocarbons in snow water and river water, and the relative recoveries corresponding to 2 and 5 μg/L were in the range of 80–116%. The repeatability of extraction and preparation of the device was investigated and the relative standard deviations (n = 3) were less than 3.6 and 5.2%. [ABSTRACT FROM AUTHOR]

Abstract: Advances in technology coupled with the availability of low‐cost sensors have resulted in the continuous generation of large time series from several sources. In order to visually explore and compare these time series at different scales, analysts need to execute online analytical processing (OLAP) queries that include constraints and group‐by's at multiple temporal hierarchies. Effective visual analysis requires these queries to be interactive. However, while existing OLAP cube‐based structures can support interactive query rates, the exponential memory requirement to materialize the data cube is often unsuitable for large data sets. Moreover, none of the recent space‐efficient cube data structures allow for updates. Thus, the cube must be re‐computed whenever there is new data, making them impractical in a streaming scenario. We propose Time Lattice, a memory‐efficient data structure that makes use of the implicit temporal hierarchy to enable interactive OLAP queries over large time series. Time Lattice is a subset of a fully materialized cube and is designed to handle fast updates and streaming data. We perform an experimental evaluation which shows that the space efficiency of the data structure does not hamper its performance when compared to the state of the art. In collaboration with signal processing and acoustics research scientists, we use the Time Lattice data structure to design the Noise Profiler, a web‐based visualization framework that supports the analysis of noise from cities. We demonstrate the utility of Noise Profiler through a set of case studies. [ABSTRACT FROM AUTHOR]

The Model-Driven Architecture (MDA) is an approach that aligns modeling and automation for software development. By applying such an approach to data warehouse (DW) projects, we can minimize a great deal of time and cost. Furthermore, most of OnLine Analytical Processing (OLAP) platforms seem to be like black boxes that provide wizards only to business intelligence developers to create and manipulate OLAP objects without allowing their sustainability and migration from a platform to another. That is why many works in the literature have proposed using the MDA approach in DW projects. However, most of them have mainly focused on the generation of the DW relational model from the conceptual one, and they overlooked the OLAP model and the cube implementation. To deal with this problem, we propose in this paper an MDA solution to automate the process of getting OLAP cube and its implementation through a set of metamodels and automatic transformations among them. In fact, the proposal generates the OLAP and DW relational models (PSMs) from the conceptual one, using also a PDM model that describes the target business intelligence platform. After that, the source code to create the cube is got from both PSM models. For this aim, we define a set of transformation rules implemented using the Atlas transformation language. Finally, a case study will be provided to validate our approach. [ABSTRACT FROM AUTHOR]

Online NMR spectroscopy is an excellent tool to study complex reacting multicomponent mixtures and gain process insight and understanding. For online studies under process conditions, flow NMR probes can be used in a wide range of temperature and pressure. This paper compiles the most important aspects towards quantitative process NMR spectroscopy in complex multicomponent mixtures and provides examples. After NMR spectroscopy is introduced as an online method and for technical samples without sample preparation in deuterated solvents, influences of the residence time distribution, pre-magnetization, and cell design are discussed. NMR acquisition and processing parameters as well as data preparation methods are presented and the most practical data analysis strategies are introduced. [ABSTRACT FROM AUTHOR]

In order to explore the most current information and react faster to changing business conditions, organizations consider real-time data warehousing a powerful technique to achieve operational business intelligence (BI). We propose in this paper a novel real-time data warehouse (RTDW) framework based on the virtualization concept. Our approach introduces a conceptual modelling technique, known as ring modelling, for real-time data management and multidimensional analysis. This technique produces a flexible semi-structured data model that accommodates unknown business process data and relationships as they evolve, handles schema changes and aggregate-management efficiently, and scales well with the large size of increasing data volumes. With the help of a telecommunication business example, We evaluated our proposed approach in an extensive experimental study where we compared our approach Ring Model with existing structured multidimensional conceptual models (MCMs), i.e. relational OLAP and multidimensional OLAP, and with semi-structured MCM, i.e. XML Cubes, in terms of scalability, data storage estimations, data updates loading time, and query response times. Our performance results show that encouraging speedups are achieved. [ABSTRACT FROM AUTHOR]

In this paper, we propose a methodology for providing linguistic answers to queries involving the comparison of time series obtained from data cubes with time dimension. Time series related to events which are interesting for the user are obtained by querying data cubes using OnLine Analytical Processing (OLAP) operations on the time dimension. The comparison of these query results can be summarized so that an appropriate short linguistic description of the series is provided to the user. Our approach is based on linguistically quantified statements and pointwise definitions of the degree and sign of local change. Our linguistic summaries are well suited to be included in an interface layer of a data warehouse system, improving the quality of human-machine interaction and the understandability of the results. © 2011 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Query answering requirements for a knowledge-based treatment of user requests led us to introduce the concept of closure of an intuitionistic fuzzy set over a universe that has a hierarchical structure. We recommend the automatic analysis of queries according to concepts defined as part of knowledge-based hierarchies to guide the query answering as part of an integrated database environment with the aid of hierarchical intuitionistic fuzzy sets (H-IFS). In this paper based on the notion of H-IFS, we propose an ad hoc utility build on top of Oracle10g that allows us to enhance the query capabilities of by providing better, knowledgeable, and optimized answers to user's requests. © 2010 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Several classes of scientific and commercial applications require the execution of a large number of independent tasks. One highly successful and low-cost mechanism for acquiring the necessary computing power for these applications is the ‘public-resource computing’, or ‘desktop Grid’ paradigm, which exploits the computational power of private computers. So far, this paradigm has not been applied to data mining applications for two main reasons. First, it is not straightforward to decompose a data mining algorithm into truly independent sub-tasks. Second, the large volume of the involved data makes it difficult to handle the communication costs of a parallel paradigm. This paper introduces a general framework for distributed data mining applications called Mining@home. In particular, we focus on one of the main data mining problems: the extraction of closed frequent itemsets from transactional databases. We show that it is possible to decompose this problem into independent tasks, which however need to share a large volume of the data. We thus introduce a data-intensive computing network, which adopts a P2P topology based on super peers with caching capabilities, aiming to support the dissemination of large amounts of information. Finally, we evaluate the execution of a pattern extraction task on such network. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The present paper deals with the problem of an assessment of symptoms in medical diagnosis. A unified interpretation of symptoms is necessary to estimate their significance in a diagnosis. Yet, even if they are properly defined, different evaluations of them based on experts' knowledge or statistical estimation are possible. The present study aims at combining evaluations that may originate from an expert or can be found from statistical features of the data, as well as those determined for ‘easy’ and ‘difficult’ diagnostic cases. A model of diagnostic inference is proposed in the framework of the Dempster–Shafer theory extended for fuzzy focal elements. The basic probability assignment defined in this theory estimates weights of symptoms. Two basic probability assignments can be created and then combined. In this way weights of symptoms represent knowledge common for two kinds of data or obtained from an expert and from data. Thus, a combination of heuristics and data mining results becomes possible. An algorithm of the basic probability assignment calculation is suggested and tested for medical data: a database from the internet and individually gathered data. [ABSTRACT FROM AUTHOR]

This publication presents the architecture integration and implementation of various modules in Sparta framework. Sparta is a trajectory engine that is hooked to an Online Analytical Processing (OLAP) database for Multi-dimensional analysis capability. OLAP is an Online Analytical Processing database that has a comprehensive list of atmospheric entry probes and their vehicle dimensions, trajectory data, aero-thermal data and material properties like Carbon, Silicon and Carbon-Phenolic based Ablators. An approach is presented for dynamic TPS design. OLAP has the capability to run in one simulation several different trajectory conditions and the output is stored back into the database and can be queried for appropriate trajectory type. An OLAP simulation can be setup by spawning individual threads to run for three types of trajectory: Nominal, Undershoot and Overshoot trajectory. Sparta graphical user interface provides capabilities to choose from a list of flight vehicles or enter trajectory and geometry information of a vehicle in design. DOTNET framework acts as a middleware layer between the trajectory engine and the user interface and also between the web user interface and the OLAP database. Trajectory output can be obtained in TecPlot format, Excel output or in a KML (Keyhole Markup Language) format. Framework employs an API (application programming interface) to convert trajectory data into a formatted KML file that is used by Google Earth for simulating Earth-entry fly-by visualizations. [ABSTRACT FROM AUTHOR]

OLAP query processing is critical for enterprise grids. Capitalizing on our experience with the ParGRES database cluster, we propose a middleware solution, GParGRES, which exploits database replication and inter- and intra-query parallelism to efficiently support OLAP queries in a grid. GParGRES is designed as a wrapper that enables the use of ParGRES in PC clusters of a grid (in our case, Grid5000). Our approach has two levels of query splitting: grid-level splitting, implemented by GParGRES, and node-level splitting, implemented by ParGRES. GParGRES has been partially implemented as database grid services compatible with existing grid solutions such as the open grid service architecture and the Web services resource framework. We give preliminary experimental results obtained with two clusters of Grid5000 using queries of the TPC-H Benchmark. The results show linear or almost linear speedup in query execution, as more nodes are added in all tested configurations. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Data cubes for OLAP (On-Line Analytical Processing) often need to be constructed from data located in several distributed and autonomous information sources. Such a data integration process is challenging due to semantic, syntactic, and structural heterogeneity among the data. While XML (extensible markup language) is the de facto standard for data exchange, the three types of heterogeneity remain. Moreover, popular path-oriented XML query languages, such as XQuery, require the user to know in much detail the structure of the documents to be processed and are, thus, effectively impractical in many real-world data integration tasks. Several Lowest Common Ancestor (LCA)-based XML query evaluation strategies have recently been introduced to provide a more structure-independent way to access XML documents. We shall, however, show that this approach leads in the context of certain—not uncommon—types of XML documents to undesirable results. This article introduces a novel high-level data extraction primitive that utilizes the purpose-built Smallest Possible Context (SPC) query evaluation strategy. We demonstrate, through a system prototype for OLAP data cube construction and a sample application in informetrics, that our approach has real advantages in data integration. [ABSTRACT FROM AUTHOR]

This article introduces a nested clustering technique and its application to the analysis of freeway operating condition. A clustering model is developed using the traffic data (flow, speed, occupancy) collected by the detectors and aggregated to 5-minute increments. An optimum fit of the statistical characteristics of the data set is provided by the model based on the Bayesian Information Criterion and the ratio of changes in dispersion measurement. This technique is flexible in determining the number of clusters based on the statistical characteristics of the data. Tests on multiple sites with varying operating conditions have attested to its effectiveness as a data mining tool for the analysis of freeway operating condition. [ABSTRACT FROM AUTHOR]

Machine learning (ML) and artificial intelligence (AI) methods are having an increasing impact on both the theory and the practice of health, safety, and environmental risk analysis. Such rules are easily interpretable by humans risk managers, unlike the predictions from some other machine learning (ML) predictive analytics technologies, such as deep learning. The spatiotemporal risk analysis and decision support system includes an influenza transmission risk simulation model that combines traditional compartmental modeling with a small-world social network model to better capture the observed dynamics of influenza spread. [Extracted from the article]

In this paper, a model of a data warehouse based on an ERP system is presented. A traditional data warehouse has the function of providing data to decision support systems (DSS) or online analytical processing systems (OLAP) but not knowledge. This paper proposes that an existing data warehouse can be extended to create a knowledge warehouse for knowledge management. The knowledge warehouse can manage not only data and information but also the knowledge assets of an enterprise. Both tacit knowledge and explicit knowledge can be analysed, integrated, and converted; new knowledge can be created through the synergistic interactions within the knowledge warehouse. In this paper, knowledge warehouse architecture based on ERP, how it works, and related technologies are discussed. The knowledge warehouse will provide more effective support for DSS and OLAP. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Over the past 10 years, various kinds of logistics information systems have been developed to store and process all sorts of data and information to support daily logistics operations. However, the logistics planning or decision-making of logistics activity is still executed manually. In this paper, a real-time knowledge-based system (RKBS) is designed to support logistic service providers in making decisions during the stage of logistics planning and operation by extracting, sharing and storing real-time logistics knowledge. The proposed system, which is suitable for usage in different business processes in a warehouse operating environment, is developed by integrating radio-frequency identification, online analytical processing, case-based reasoning technologies, and a branch-and-bound resource-route-optimizing programming model. Through applying the RKBS in GENCO, a US-based logistics service company, the overall logistics servicing level is enhanced through accurate decision-making and planning of warehouse operations. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Multidimensional data analysis or On-line analytical processing (OLAP) offers a single subject-oriented source for analyzing summary data based on various dimensions. We demonstrate that the OLAP approach gives a promising starting point for advanced analysis and comparison among summary data in informetrics applications. At the moment there is no single precise, commonly accepted logical/conceptual model for multidimensional analysis. This is because the requirements of applications vary considerably. We develop a conceptual/logical multidimensional model for supporting the complex and unpredictable needs of informetrics. Summary data are considered with respect of some dimensions. By changing dimensions the user may construct other views on the same summary data. We develop a multidimensional query language whose basic idea is to support the definition of views in a way, which is natural and intuitive for lay users in the informetrics area. We show that this view-oriented query language has a great expressive power and its degree of declarativity is greater than in contemporary operation-oriented or SQL (Structured Query Language)-like OLAP query languages. [ABSTRACT FROM AUTHOR]