Query: Data Warehouse Life-Cycle and Design

Relevant passages:
Conceptual modeling for ETL processes <=SEP=> Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. In this paper, we focus on the problem of the definition of ETL activities and provide formal foundations for their conceptual representation. The proposed conceptual model is (a) customized for the tracing of inter-attribute relationships and the respective ETL activities in the early stages of a data warehouse project; (b) enriched with a 'palette' of a set of frequently used ETL activities, like the assignment of surrogate keys, the check for null values, etc; and (c) constructed in a customizable and extensible manner, so that the designer can enrich it with his own re-occurring patterns for ETL activities.

Beyond data warehousing: what's next in business intelligence? <=SEP=> During the last ten years the approach to business management has deeply changed, and companies have understood the importance of enforcing achievement of the goals defined by their strategy through metrics-driven management. The DW process, though supporting bottom-up extraction of information from data, fails in top-down enforcing the company strategy. A new approach to BI, called Business Performance Management (BPM), is emerging from this framework: it includes DW but it also requires a reactive component capable of monitoring the time-critical operational processes to allow tactical and operational decision-makers to tune their actions according to the company strategy. The aim of this paper is to encourage the research community to acknowledge the coming of a second era in BI, to propose a general architecture for BPM, and to lay the premises for investigating the most challenging of the related issues.

A UML Based Approach for Modeling ETL Processes in Data Warehouses <=SEP=> Data warehouses (DWs) are complex computer systems whose main goal is to facilitate the decision making process of knowledge workers. ETL (Extraction-Transformation-Loading) processes are responsible for the extraction of data from heterogeneous operational data sources, their transformation (conversion, cleaning, normalization, etc.) and their loading into DWs. ETL processes are a key component of DWs because incorrect or misleading data will produce wrong business decisions, and therefore, a correct design of these processes at early stages of a DW project is absolutely necessary to improve data quality. However, not much research has dealt with the modeling of ETL processes. In this paper, we present our approach, based on the Unified Modeling Language (UML), which allows us to accomplish the conceptual modeling of these ETL processes together with the conceptual schema of the target DW in an integrated manner. We provide the necessary mechanisms for an easy and quick specification of the common operations defined in these ETL processes such as, the integration of different data sources, the transformation between source and target attributes, the generation of surrogate keys and so on. Moreover, our approach allows the designer a comprehensive tracking and documentation of entire ETL processes, which enormously facilitates the maintenance of these processes. Another advantage of our proposal is the use of the UML (standardization, ease-of-use and functionality) and the seamless integration of the design of the ETL processes with the DW conceptual schema. Finally, we show how to use our integrated approach by using a well-known modeling tool such as Rational Rose.

Potter's Wheel: An Interactive Data Cleaning System <=SEP=> Cleaning data of errors in structure and content is important for data warehousing and integration. Current solutions for data cleaning involve many iterations of data “auditing” to find errors, and long-running transformations to fix them. Users need to endure long waits, and often write complex transformation scripts. We present Potter’s Wheel, an interactive data cleaning system that tightly integrates transformation and discrepancy detection. Users gradually build transformations to clean the data by adding or undoing transforms on a spreadsheet-like interface; the effect of a transform is shown at once on records visible on screen. These transforms are specified either through simple graphical operations, or by showing the desired effects on example data values. In the background, Potter’s Wheel automatically infers structures for data values in terms of user-defined domains, and accordingly checks for constraint violations. Thus users can gradually build a transformation as discrepancies are found, and clean the data without writing complex programs or enduring long delays.

Modeling ETL activities as graphs <=SEP=> Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. In this paper, we focus on the logical design of the ETL scenario of a data warehouse. Based on a formal logical model that includes the data stores, activities and their constituent parts, we model an ETL scenario as a graph, which we call the Architecture Graph. We model all the aforementioned entities as nodes and four different kinds of relationships (instance-of, part-of, regulator and provider relationships) as edges. In addition, we provide simple graph transformations that reduce the complexity of the graph. Finally, in order to support the engineering of the design and the evolution of the warehouse, we introduce specific importance metrics, namely dependence and responsibility, to measure the degree to which entities are bound to each other.

A survey of B-tree locking techniques <=SEP=> B-trees have been ubiquitous in database management systems for several decades, and they are used in other storage systems as well. Their basic structure and basic operations are well and widely understood including search, insertion, and deletion. Concurrency control of operations in B-trees, however, is perceived as a difficult subject with many subtleties and special cases. The purpose of this survey is to clarify, simplify, and structure the topic of concurrency control in B-trees by dividing it into two subtopics and exploring each of them in depth.

Data Preparation for Data Mining <=SEP=> Senior Editor: Diane D. Cerra Director of Production & Manufacturing: Yonie Overton Production Editor: Edward Wade Editorial Assistant: Belinda Breyer Cover Design: Wall-To-Wall Studios Cover Photograph: © 1999 PhotoDisc, Inc. Text Design & Composition: Rebecca Evans & Associates Technical Illustration: Dartmouth Publishing, Inc. Copyeditor: Gary Morris Proofreader: Ken DellaPenta Indexer: Steve Rath Printer: Courier Corp.

RefSeer: A citation recommendation system <=SEP=> Citations are important in academic dissemination. To help researchers check the completeness of citations while authoring a paper, we introduce a citation recommendation system called RefSeer. Researchers can use it to find related works to cited while authoring papers. It can also be used by reviewers to check the completeness of a paper's references. RefSeer presents both topic based global recommendation and also citation-context based local recommendation. By evaluating the quality of recommendation, we show that such recommendation system can recommend citations with good precision and recall. We also show that our recommendation system is very efficient and scalable.

A robust-coded pattern projection for dynamic 3D scene measurement <=SEP=> This paper presents a new coded structured light pattern which permits to solve the correspondence problem by a single shot and without using geometrical constraints. The pattern is composed by the projection of a grid made by coloured slits in such a way that each slit with its two neighbours appears only once in the pattern. The technique proposed permits a rapid and robust 3D scene measurement, even with moving objects. Ó 1998 Elsevier Science B.V. All rights reserved.

A Comparative Study of Psychosocial Interventions for Internet Gaming Disorder Among Adolescents Aged 13–17 Years <=SEP=> The present study is a quasi-experimental, prospective study of interventions for internet gaming disorder (IGD). One hundred four parents and their adolescent children were enrolled and allocated to one of the four treatment groups; 7-day Siriraj Therapeutic Residential Camp (S-TRC) alone, 8-week Parent Management Training for Game Addiction (PMT-G) alone, combined S-TRC and PMT-G, and basic psychoeducation (control). The severity of IGD was measured by the Game Addiction Screening Test (GAST). The mean difference among groups in GAST scores was statistically significant, with P values of < 0.001, 0.002, and 0.005 at 1, 3, and 6 months post-intervention, respectively. All groups showed improvement over the control group. The percentage of adolescents who remained in the addicted or probably addicted groups was less than 50% in the S-TRC, PMT-G, and combined groups. In conclusion, both S-TRC and PMT-G were effective psychosocial interventions for IGD and were superior to basic psychoeducation alone.

Paper-Based Synthetic Gene Networks <=SEP=> Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides an alternate, versatile venue for synthetic biologists to operate and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze dried onto paper, enabling the inexpensive, sterile, and abiotic distribution of synthetic-biology-based technologies for the clinic, global health, industry, research, and education. For field use, we create circuits with colorimetric outputs for detection by eye and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small-molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors.

From centrality to temporary fame: Dynamic centrality in complex networks <=SEP=> We develop a new approach to the study of the dynamics of link utilization in complex networks using records of communication in a large social network. Counter to the perspective that nodes have particular roles, we find roles change dramatically from day to day. “Local hubs” have a power law degree distribution over time, with no characteristic degree value. Our results imply a significant reinterpretation of the concept of node centrality in complex networks, and among other conclusions suggest that interventions targeting hubs will have significantly less effect than previously thought. © 2006 Wiley Periodicals, Inc. Complexity 12: 000 – 000, 2006

HERZBERG ' S MOTIVATION-HYGIENE THEORY AND JOB SATISFACTION IN THE MALAYSIAN RETAIL SECTOR : THE MEDIATING EFFECT OF LOVE OF MONEY <=SEP=> This paper examines what motivates employees in the retail industry, and examines their level of job satisfaction, using Herzberg's hygiene factors and motivators. In this study, convenience sampling was used to select sales personnel from women's clothing stores in Bandar Sunway shopping mall in the state of Selangor. The results show that hygiene factors were the dominant motivators of sales personnel job satisfaction. Working conditions were the most significant in motivating sales personnel. Recognition was second, followed by company policy and salary. There is a need to delve more deeply into why salespeople place such a high importance on money. Further analysis was performed to assess how much the love of money mediates the relationship between salary and job satisfaction. Based on the general test for mediation, the love of money could explain the relationship between salary and job satisfaction. The main implication of this study is that sales personnel who value money highly are satisfied with their salary and job when they receive a raise.

Evaluation of mobile app paradigms <=SEP=> The explosion of mobile applications both in number and variety raises the need of shedding light on their architecture, composition and quality. Indeed, it is crucial to understand which mobile application paradigm fits better to what type of application and usage. Such understanding has direct consequences on the user experience, the development cost and sale revenues of mobile apps. In this paper, we identified four main mobile application paradigms and evaluated them from the developer, user and service provider viewpoints. To ensure objectivity and soundness we start by defining high level criteria and then breaking down in finer-grained criteria. After a theoretical evaluation an implementation was carried out as a practical verification. The selected application is object recognition app, which is both exciting and challenging to develop.

Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning <=SEP=> BACKGROUND
Pharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs.


OBJECTIVE
We aimed to design a deep learning model for extracting ADEs and related information such as medications and indications. Since extraction of ADE-related information includes two steps-named entity recognition and relation extraction-our second objective was to improve the deep learning model using multi-task learning between the two steps.


METHODS
We employed the dataset from the Medication, Indication and Adverse Drug Events (MADE) 1.0 challenge to train and test our models. This dataset consists of 1089 EHR notes of cancer patients and includes 9 entity types such as Medication, Indication, and ADE and 7 types of relations between these entities. To extract information from the dataset, we proposed a deep-learning model that uses a bidirectional long short-term memory (BiLSTM) conditional random field network to recognize entities and a BiLSTM-Attention network to extract relations. To further improve the deep-learning model, we employed three typical MTL methods, namely, hard parameter sharing, parameter regularization, and task relation learning, to build three MTL models, called HardMTL, RegMTL, and LearnMTL, respectively.


RESULTS
Since extraction of ADE-related information is a two-step task, the result of the second step (ie, relation extraction) was used to compare all models. We used microaveraged precision, recall, and F1 as evaluation metrics. Our deep learning model achieved state-of-the-art results (F1=65.9%), which is significantly higher than that (F1=61.7%) of the best system in the MADE1.0 challenge. HardMTL further improved the F1 by 0.8%, boosting the F1 to 66.7%, whereas RegMTL and LearnMTL failed to boost the performance.


CONCLUSIONS
Deep learning models can significantly improve the performance of ADE-related information extraction. MTL may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors. Our results can facilitate research on ADE detection, NLP, and machine learning.

Measuring level of cuteness of baby images: a supervised learning scheme <=SEP=> The attractiveness of a baby face image depends on the perception of the perceiver. However, several recent studies advocate the idea that human perceptual analysis can be approximated by statistical models. We believe that the cuteness of baby faces depends on the low level facial features extracted from different parts (e.g., mouth, eyes, nose) of the faces. In this paper, we introduce a new problem of classifying baby face images based on their cuteness level using supervised learning techniques. The proposed learning model finds the potential of a deep learning technique in measuring the level of cuteness of baby faces. Since no datasets are available to validate the proposed technique, we construct a dataset of images of baby faces, downloaded from the internet. The dataset consists of several challenges like different view-point, orientation, lighting condition, contrast and background. We annotate the data using some well-known statistical tools inherited from Reliability theory. The experiments are conducted with some well-known image features like Speeded Up Robust Feature (SURF), Histogram of Oriented Gradient (HOG), Convolutional Neural Network (CNN) on Gradient and CNN on Laplacian, and the results are presented and discussed.

An empirical analysis of smart contracts: platforms, applications, and design patterns <=SEP=> Smart contracts are computer programs that can be consistently executed by a network of mutually distrusting nodes, without the arbitration of a trusted authority. Because of their resilience to tampering, smart contracts are appealing in many scenarios, especially in those which require transfers of money to respect certain agreed rules (like in financial services and in games). Over the last few years many platforms for smart contracts have been proposed, and some of them have been actually implemented and used. We study how the notion of smart contract is interpreted in some of these platforms. Focussing on the two most widespread ones, Bitcoin and Ethereum, we quantify the usage of smart contracts in relation to their application domain. We also analyse the most common programming patterns in Ethereum, where the source code of smart contracts is available.

A 5.2mW, 0.0016% THD up to 20kHz, ground-referenced audio decoder with PSRR-enhanced class-AB 16Ω headphone amplifiers <=SEP=> A low-power ground-referenced audio decoder with PSRR-enhanced class-AB headphone amplifiers presents <;0.0016% THD in the whole audio band against the supply ripple by a negative charge-pump. Realized in the 40nm CMOS, the fully-integrated stereo decoder achieves 91dB SNDR and 100dB dynamic range while driving a 16Ω headphone load and consumes 5.2mW from a 1.8V power supply. The core area is 0.093mm2/channel only.

An operational measure of information leakage <=SEP=> Given two discrete random variables X and Y, an operational approach is undertaken to quantify the “leakage” of information from X to Y. The resulting measure ℒ(X→Y ) is called maximal leakage, and is defined as the multiplicative increase, upon observing Y, of the probability of correctly guessing a randomized function of X, maximized over all such randomized functions. It is shown to be equal to the Sibson mutual information of order infinity, giving the latter operational significance. Its resulting properties are consistent with an axiomatic view of a leakage measure; for example, it satisfies the data processing inequality, it is asymmetric, and it is additive over independent pairs of random variables. Moreover, it is shown that the definition is robust in several respects: allowing for several guesses or requiring the guess to be only within a certain distance of the true function value does not change the resulting measure.

Vehicle Detection and Tracking in Car Video Based on Motion Model <=SEP=> This paper aims at real-time in-car video analysis to detect and track vehicles ahead for safety, autodriving, and target tracing. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. We have investigated videos of day and night on different types of roads, showing that our approach is robust and effective in dealing with changes in environment and illumination and that real-time processing becomes possible for vehicle-borne cameras.

The Weak Byzantine Generals Problem <=SEP=> The Byzantine Generals Problem requires processes to reach agreement upon a value even though some of them may fad. It is weakened by allowing them to agree upon an "incorrect" value if a failure occurs. The transaction eormmt problem for a distributed database Js a special case of the weaker problem. It is shown that, like the original Byzantine Generals Problem, the weak version can be solved only ff fewer than one-third of the processes may fad. Unlike the onginal problem, an approximate solution exists that can tolerate arbaranly many failures. Categories and Subject Descnptors: D.4.5 [Operating Systems]: Reliability, F.2.m [Analysis of Algorithms and Problem Complexity]: Mzscellaneous General Terms: Reliabthty Addmonal

Action and Interaction Recognition in First-Person Videos <=SEP=> In this work, we evaluate the performance of the popular dense trajectories approach on first-person action recognition datasets. A person moving around with a wearable camera will actively interact with humans and objects and also passively observe others interacting. Hence, in order to represent real-world scenarios, the dataset must contain actions from first-person perspective as well as third-person perspective. For this purpose, we introduce a new dataset which contains actions from both the perspectives captured using a head-mounted camera. We employ a motion pyramidal structure for grouping the dense trajectory features. The relative strengths of motion along the trajectories are used to compute different bag-of-words descriptors and concatenated to form a single descriptor for the action. The motion pyramidal approach performs better than the baseline improved trajectory descriptors. The method achieves 96.7% on the JPL interaction dataset and 61.8% on our NUS interaction dataset. The same is used to detect actions in long video sequences and achieves average precision of 0.79 on JPL interaction dataset.

Wearable Electronics and Smart Textiles: A Critical Review <=SEP=> Electronic Textiles (e-textiles) are fabrics that feature electronics and interconnections woven into them, presenting physical flexibility and typical size that cannot be achieved with other existing electronic manufacturing techniques. Components and interconnections are intrinsic to the fabric and thus are less visible and not susceptible of becoming tangled or snagged by surrounding objects. E-textiles can also more easily adapt to fast changes in the computational and sensing requirements of any specific application, this one representing a useful feature for power management and context awareness. The vision behind wearable computing foresees future electronic systems to be an integral part of our everyday outfits. Such electronic devices have to meet special requirements concerning wearability. Wearable systems will be characterized by their ability to automatically recognize the activity and the behavioral status of their own user as well as of the situation around her/him, and to use this information to adjust the systems' configuration and functionality. This review focuses on recent advances in the field of Smart Textiles and pays particular attention to the materials and their manufacturing process. Each technique shows advantages and disadvantages and our aim is to highlight a possible trade-off between flexibility, ergonomics, low power consumption, integration and eventually autonomy.

Customers ’ Behavior Prediction Using Artificial Neural Network <=SEP=> In this paper, customer restaurant preference is predicted based on social media location check-ins. Historical preferences of the customer and the influence of the customer’s social network are used in combination with the customer’s mobility characteristics as inputs to the model. As the popularity of social media increases, more and more customer comments and feedback about products and services are available online. It not only becomes a way of sharing information among friends in the social network but also forms a new type of survey which can be utilized by business companies to improve their existing products, services, and market analysis. Approximately 121,000 foursquare restaurant check-ins in the Greater New York City area are used in this research. Artificial neural networks (ANN) and support vector machine (SVM) are developed to predict the customers’ behavior regarding restaurant preferences. ANN provides 93.13% average accuracy across investigated customers, compared to only 54.00% for SVM with a sigmoid kernel function.

Scene Understanding by Reasoning Stability and Safety <=SEP=> This paper presents a new perspective for 3D scene understanding by reasoning object stability and safety using intuitive mechanics. Our approach utilizes a simple observation that, by human design, objects in static scenes should be stable in the gravity field and be safe with respect to various physical disturbances such as human activities. This assumption is applicable to all scene categories and poses useful constraints for the plausible interpretations (parses) in scene understanding. Given a 3D point cloud captured for a static scene by depth cameras, our method consists of three steps: (i) recovering solid 3D volumetric primitives from voxels; (ii) reasoning stability by grouping the unstable primitives to physically stable objects by optimizing the stability and the scene prior; and (iii) reasoning safety by evaluating the physical risks for objects under physical disturbances, such as human activity, wind or earthquakes. We adopt a novel intuitive physics model and represent the energy landscape of each primitive and object in the scene by a disconnectivity graph (DG). We construct a contact graph with nodes being 3D volumetric primitives and edges representing the supporting relations. Then we adopt a Swendson–Wang Cuts algorithm to partition the contact graph into groups, each of which is a stable object. In order to detect unsafe objects in a static scene, our method further infers hidden and situated causes (disturbances) in the scene, and then introduces intuitive physical mechanics to predict possible effects (e.g., falls) as consequences of the disturbances. In experiments, we demonstrate that the algorithm achieves a substantially better performance for (i) object segmentation, (ii) 3D volumetric recovery, and (iii) scene understanding with respect to other state-of-the-art methods. We also compare the safety prediction from the intuitive mechanics model with human judgement.

Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. <=SEP=> Understanding the long association pathways that convey cortical connections is a critical step in exploring the anatomic substrates of cognition in health and disease. Diffusion tensor imaging (DTI) is able to demonstrate fibre tracts non-invasively, but present approaches have been hampered by the inability to visualize fibres that have intersecting trajectories (crossing fibres), and by the lack of a detailed map of the origins, course and terminations of the white matter pathways. We therefore used diffusion spectrum imaging (DSI) that has the ability to resolve crossing fibres at the scale of single MRI voxels, and identified the long association tracts in the monkey brain. We then compared the results with available expositions of white matter pathways in the monkey using autoradiographic histological tract tracing. We identified 10 long association fibre bundles with DSI that match the observations in the isotope material: emanating from the parietal lobe, the superior longitudinal fasciculus subcomponents I, II and III; from the occipital-parietal region, the fronto-occipital fasciculus; from the temporal lobe, the middle longitudinal fasciculus and from rostral to caudal, the uncinate fasciculus, extreme capsule and arcuate fasciculus; from the occipital-temporal region, the inferior longitudinal fasciculus; and from the cingulate gyrus, the cingulum bundle. We suggest new interpretations of the putative functions of these fibre bundles based on the cortical areas that they link. These findings using DSI and validated with reference to autoradiographic tract tracing in the monkey represent a considerable advance in the understanding of the fibre pathways in the cerebral white matter. By replicating the major features of these tracts identified by histological techniques in monkey, we show that DSI has the potential to cast new light on the organization of the human brain in the normal state and in clinical disorders.

The role of the self in mindblindness in autism <=SEP=> Since its inception the 'mindblindness' theory of autism has greatly furthered our understanding of the core social-communication impairments in autism spectrum conditions (ASC). However, one of the more subtle issues within the theory that needs to be elaborated is the role of the 'self'. In this article, we expand on mindblindness in ASC by addressing topics related to the self and its central role in the social world and then review recent research in ASC that has yielded important insights by contrasting processes relating to both self and other. We suggest that new discoveries lie ahead in understanding how self and other are interrelated and/or distinct, and how understanding atypical self-referential and social-cognitive mechanisms may lead to novel ideas as to how to facilitate social-communicative abilities in ASC.

A review of functional Near-Infrared Spectroscopy measurements in naturalistic environments <=SEP=> The development of novel miniaturized wireless and wearable functional Near-Infrared Spectroscopy (fNIRS) devices have paved the way to new functional brain imaging that can revolutionize the cognitive research fields. Over the past few decades, several studies have been conducted with conventional fNIRS systems that have demonstrated the suitability of this technology for a wide variety of populations and applications, to investigate both the healthy brain and the diseased brain. However, what makes wearable fNIRS even more appealing is its capability to allow more ecologically-valid measurements in everyday life scenarios that are not possible with other gold-standard neuroimaging modalities, such as functional Magnetic Resonance Imaging. This can have

Programming with Abstract Data Types <=SEP=> The motivation behind the work in very-high-level languages is to ease the programming task by providing the programmer with a language containing primitives or abstractions suitable to his problem area. The programmer is then able to spend his effort in the right place; he concentrates on solving his problem, and the resulting program will be more reliable as a result. Clearly, this is a worthwhile goal.
 Unfortunately, it is very difficult for a designer to select in advance all the abstractions which the users of his language might need. If a language is to be used at all, it is likely to be used to solve problems which its designer did not envision, and for which the abstractions embedded in the language are not sufficient.
 This paper presents an approach which allows the set of built-in abstractions to be augmented when the need for a new data abstraction is discovered. This approach to the handling of abstraction is an outgrowth of work on designing a language for structured programming. Relevant aspects of this language are described, and examples of the use and definitions of abstractions are given.

The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings <=SEP=> The increasing use of web 2.0 applications has generated numerous online user reviews. Prior studies have revealed the influence of user-generated reviews on the sales of products such as CDs, books, and movies. However, the influence of online user-generated reviews in the tourism industry is still largely unknown both to tourism researchers and practitioners. To bridge this knowledge gap in tourism management, we conducted an empirical study to identify the impact of online user-generated reviews on business performance using data extracted from a major online travel agency in China. The empirical findings show that traveler reviews have a significant impact on online sales, with a 10 percent increase in traveler review ratings boosting online bookings by more than five percent. Our results highlight the importance of online user-generated reviews to business performance in tourism. 2010 Elsevier Ltd. All rights reserved.