Log File Data Research Articles

Using the EDGAR Log File Data Set

The SEC's EDGAR log file data set is a collection of web server log files that allow researchers to study the demand for SEC filings. This multiple terabyte data set provides researchers with a direct measure of demand for financial reports, but the log files must be filtered to remove downloads by computer programs (or robots), and the sheer size of the files presents big data challenges. This paper compares three methods for counting human views in the EDGAR log files and aggregates the data on a filing-day basis so that it is accessible to desktop hardware and statistical analysis software. Overall, the three methods agree on the robot-human classification for 96 percent of users, but for sample 10-K filings, they can disagree by up to 27 percent. Download counts may be biased by up to 36 percent if multiple views by the same user are counted. Ryans's 2017 method eliminates multiple download counting and appears to effectively classify robots in cases of disagreement among the measures. The choice of measure may be particularly important when studying demand for Forms 10-K, 10-Q, 4, 13F-HR, as well as SEC comment letters. The aggregated data and sample code are available from the author.

Effect of a Binocular iPad Game vs Part-time Patching in Children Aged 5 to 12 Years With Amblyopia

A binocular approach to treating anisometropic and strabismic amblyopia has recently been advocated. Initial studies have yielded promising results, suggesting that a larger randomized clinical trial is warranted. To compare visual acuity (VA) improvement in children with amblyopia treated with a binocular iPad game vs part-time patching. A multicenter, noninferiority randomized clinical trial was conducted in community and institutional practices from September 16, 2014, to August 28, 2015. Participants included 385 children aged 5 years to younger than 13 years with amblyopia (20/40 to 20/200, mean 20/63) resulting from strabismus, anisometropia, or both. Participants were randomly assigned to either 16 weeks of a binocular iPad game prescribed for 1 hour a day (190 participants; binocular group) or patching of the fellow eye prescribed for 2 hours a day (195 participants; patching group). Study follow-up visits were scheduled at 4, 8, 12, and 16 weeks. A modified intent-to-treat analysis was performed on participants who completed the 16-week trial. Binocular iPad game or patching of the fellow eye. Change in amblyopic-eye VA from baseline to 16 weeks. Of the 385 participants, 187 were female (48.6%); mean (SD) age was 8.5 (1.9) years. At 16 weeks, mean amblyopic-eye VA improved 1.05 lines (2-sided 95% CI, 0.85-1.24 lines) in the binocular group and 1.35 lines (2-sided 95% CI, 1.17-1.54 lines) in the patching group, with an adjusted treatment group difference of 0.31 lines favoring patching (upper limit of the 1-sided 95% CI, 0.53 lines). This upper limit exceeded the prespecified noninferiority limit of 0.5 lines. Only 39 of the 176 participants (22.2%) randomized to the binocular game and with log file data available performed more than 75% of the prescribed treatment (median, 46%; interquartile range, 20%-72%). In younger participants (aged 5 to <7 years) without prior amblyopia treatment, amblyopic-eye VA improved by a mean (SD) of 2.5 (1.5) lines in the binocular group and 2.8 (0.8) lines in the patching group. Adverse effects (including diplopia) were uncommon and of similar frequency between groups. In children aged 5 to younger than 13 years, amblyopic-eye VA improved with binocular game play and with patching, particularly in younger children (age 5 to <7 years) without prior amblyopia treatment. Although the primary noninferiority analysis was indeterminate, a post hoc analysis suggested that VA improvement with this particular binocular iPad treatment was not as good as with 2 hours of prescribed daily patching. http://www.clinicaltrials.gov Identifier: NCT02200211.

What role for libraries in learning analytics?

Purpose – The purpose of this paper is to provide an overview of the development of an institution wide approach to learning analytics at the University of Wollongong (UOW) and the inclusion of library data drawn from the Library Cube. Design/methodology/approach – The Student Support and Education Analytics team at UOW is tasked with creating policy, frameworks and infrastructure for the systematic capture, mapping and analysis of data from the across the university. The initial data set includes: log file data from Moodle sites, Library Cube, student administration data, tutorials and student support service usage data. Using the learning analytics data warehouse UOW is developing new models for analysis and visualisation with a focus on the provision of near real-time data to academic staff and students to optimise learning opportunities. Findings – The distinct advantage of the learning analytics model is that the selected data sets are updated weekly, enabling near real-time monitoring and intervention where required. Inclusion of library data with the other often disparate data sets from across the university has enabled development of a comprehensive platform for learning analytics. Future work will include the development of predictive models using the rapidly growing learning analytics data warehouse. Practical implications – Data warehousing infrastructure, the systematic capture and exporting of relevant library data sets are requisite for the consideration of library data in learning analytics. Originality/value – What was not anticipated five years ago when the Value Cube was first realised, was the development of learning analytic services at UOW. The Cube afforded University of Wollongong Library considerable advantage: the framework for data harvesting and analysis was established, ready for inclusion within learning analytics data sets and subsequent reporting to faculty.

Get with the program: Adherence to a smartphone app for smoking cessation

IntroductionAlthough engagement is generally predictive of positive outcomes in technology-based behavioral change interventions, engagement measures remain largely atheoretical and lack treatment-specificity. This study examines the extent to which adherence measures based on the underlying behavioral change theory of an Acceptance and Commitment Therapy (ACT) app for smoking cessation predict smoking outcomes, and user characteristics associated with adherence. MethodsStudy sample was adult daily smokers in a single arm pilot study (n=84). Using the app's log file data, we examined measures of adherence to four key components of the ACT behavior change model as predictors of smoking cessation and reduction. We also examined baseline user characteristics associated with adherence measures that predict smoking cessation. ResultsFully adherent users (24%) were over four times more likely to quit smoking (OR=4.45; 95% CI=1.13, 17.45; p=0.032). Both an increase in tracking the number of urges passed (OR=1.02; 95% CI=1.00, 1.03; p=0.043) and ACT modules completed (OR=1.27; 95% CI=1.01, 1.60; p=0.042) predicted cessation. Lower baseline acceptance of cravings was associated with over four times higher odds of full adherence (OR=4.59; 95% CI=1.35, 15.54; p=0.014). ConclusionsFull adherence and use of specific ACT theory-based components of the app predicted quitting. Consistent with ACT theory, users with low acceptance were most likely to adhere to the app. Further research is needed on ways to promote app engagement.

Towards general models of effective science inquiry in virtual performance assessments

AbstractRecent interest in online assessment of scientific inquiry has led to several new online systems that attempt to assess these skills, but producing models that detect when students are successfully practising these skills can be challenging. In this paper, we study models that assess student inquiry in an immersive virtual environment, where a student navigates an avatar around a world, speaking to in‐game characters, collecting samples and conducting scientific tests with those samples in the virtual laboratory. To this goal, we leverage log file data from nearly 2000 middle school students using virtual performance assessment (VPA), a software system where students practice inquiry skills in different virtual scenarios. We develop models of student interaction within VPA that predict whether a student will successfully conduct scientific inquiry. Specifically, we identify behaviours that lead to distinguishing causal from non‐causal factors to identify a correct final conclusion and to design a causal explanation about these conclusions. We then demonstrate that these models can be adapted with minimal effort between VPA scenarios. We conclude with discussions of how these models serve as a tool for better understanding scientific inquiry in virtual environments and as a platform for the future design of evidence‐based interventions.

Efficient Extraction for Mobile Web Access Log with Caching Strategy

Mobile web access log file plays an important role in the analysis about demand of mobile terminal market or user behavior. However, the log file data is highly dimensional, disorganized and semi-structured, which heightens the difficulty of data extracting accuracy; while it generates and transmits continuously, which poses an extracting efficiency challenge. It is highly desirable to extract the information embedded in log files as disorder or hidden situation efficiently and accurately. This paper proposes an efficient extracting method for mobile web access log with cache strategy. Firstly of all, data dictionary sets are built for each kind of complex field before extracting. Then, data is extracted based on the dictionaries, and the dictionaries will be completed simultaneously. Furthermore, with the discovery of the distribution of some data in the log generally following Zipf-like distribution, cache strategy is considered to be an auxiliary way to reduce mapping time. In addition, the classical cache strategy LFU is chosen. Ultimately, the experiment shows that the data could be extracted from the log accurately, and the extracting efficiency speeds up remarkably with LFU cache strategy.

The Accuracy of Self-Reported Internet Use—A Validation Study Using Client Log Data

ABSTRACTThe vast majority of empirical research on online communication, or media use in general, relies on self-report measures instead of behavioral data. Previous research has shown that the accuracy of these self-report measures can be quite low, and both over- and underreporting of media use are commonplace. This study compares self-reports of Internet use with client log files from a large household sample. Results show that the accuracy of self-reported frequency and duration of Internet use is quite low, and that survey data are only moderately correlated with log file data. Moreover, there are systematic patterns of misreporting, especially overreporting, rather than random deviations from the log files. Self-reports for specific content such as social network sites or video platforms seem to be more accurate and less consistently biased than self-reports of generic frequency or duration of Internet use. The article closes by demonstrating the consequences of biased self-reports and discussing possible solutions to the problem.

Identifying the Effective Parameters for Vertical Handover in Cellular Networks Using Data Mining Techniques

The need for seamless mobility within the heterogeneous environment of cellular networks imposed the need for finding different vertical handover (VHO) mechanisms to select the best network. The selection process is based on different factors, such as: cost, battery status of Mobile Terminal (MT), the capacity of each network link, available bandwidth (ABW), received signal strength (RSS), etc. However, the major problem here is to find the most effective parameters for VHO and their priorities for these decision mechanisms. Besides, it would be useful to know the values of these parameters for data mining (DM). For this purpose, we collected real log data server of two mobile telecom companies in Lebanon, Touch and Alfa for Global System for Mobile Communications (GSM) and Universal Mobile Telecommunications System (UMTS) networks. After preprocessing and discretizing the data, frequent patterns (FP) were extracted to summarize the log data files. The summarized data was then analyzed by using descriptive and visualization techniques in order to find the most effective parameters for handover (HO) process. Three effective parameters were obtained: the Received Signal Strength (RxLev/RSCP), the Available Bandwidth (ABW) and the Received Signal Quality (RxQual/EcNo). Results showed that they cooperatively work together to accomplish the same task. This paper provide an effective solution to identify the most valuable factors for vertical handover mechanism in telecommunication area by using frequent pattern mining.

A Framework for an Efficient Knowledge Mining Technique of Web Page Reorganisation using Splay Tree

Background/Objectives: Web Usage Mining (WUM) is one of the categories of web mining that identifies user patterns of web data, with the help of knowledge acquires from web logs. Methods/Statistical Analysis: The structure of the web site has to be reorganised to suit the user requirements to facilitate the user for the required pages with less page access delay. The Splay trees are efficient balanced trees when total running time is the measure of interest. Findings: The motive of mining is to find users’ access models automatically and quickly from the vast Web log data, like frequently accessed pages and time spent on those pages. Web usage mining consist of three phases namely Data pre-processing, Pattern discovery and Pattern analysis. Pre-processing tasks are used to translate unprocessed log files which are composed from web server into structured log file data. Pre-processed log file data are used for further process of web usage mining. This paper present the pre-processing technique and an approach for re-organisation of website based on the access frequency of web pages using splay tree structure. Application/Improvements: The nodes of the splay tree can be added with the information about priority of recently accessed web pages to reduce the page access delay.

Automated analysis of Kokee–Wettzell Intensive VLBI sessions—algorithms, results, and recommendations

The time-dependent variations in the rotation and orientation of the Earth are represented by a set of Earth Orientation Parameters (EOP). Currently, Very Long Baseline Interferometry (VLBI) is the only technique able to measure all EOP simultaneously and to provide direct observation of universal time, usually expressed as UT1-UTC. To produce estimates for UT1-UTC on a daily basis, 1-h VLBI experiments involving two or three stations are organised by the International VLBI Service for Geodesy and Astrometry (IVS), the IVS Intensive (INT) series. There is an ongoing effort to minimise the turn-around time for the INT sessions in order to achieve near real-time and high quality UT1-UTC estimates. As a step further towards true fully automated real-time analysis of UT1-UTC, we carry out an extensive investigation with INT sessions on the Kokee–Wettzell baseline. Our analysis starts with the first versions of the observational files in S- and X-band and includes an automatic group delay ambiguity resolution and ionospheric calibration. Several different analysis strategies are investigated. In particular, we focus on the impact of external information, such as meteorological and cable delay data provided in the station log-files, and a priori EOP information. The latter is studied by extensive Monte Carlo simulations. Our main findings are that it is easily possible to analyse the INT sessions in a fully automated mode to provide UT1-UTC with very low latency. The information found in the station log-files is important for the accuracy of the UT1-UTC results, provided that the data in the station log-files are reliable. Furthermore, to guarantee UT1-UTC with an accuracy of less than 20 μs, it is necessary to use predicted a priori polar motion data in the analysis that are not older than 12 h.

Computer-generated log-file analyses as a window into students' minds? A showcase study based on the PISA 2012 assessment of problem solving

This paper aims at showcasing the potential of log-file analyses by capitalizing on the computer-based assessment of complex problem solving (CPS) in the 2012 cycle of the Programme for International Student Assessment (PISA). We analyzed log-file data from the CPS item Climate Control encompassing N = 16,219 students from 44 countries and economies. In Research Question 1, we related the application of an optimal exploration strategy (i.e., vary-one-thing-at-at-time, VOTAT) to performance on this specific problem and to overall level of CPS proficiency, both on an individual and on the country level. In Research Question 2, we identified several groups of students with different levels of non-mastery on a fine-grained level. Results indicated that (1) the VOTAT strategy was strongly related to performance on the Climate Control item as well as to overall problem solving proficiency, and (2) that there were different levels of non-mastery that ranged from applying no systematic strategic behavior to actually applying VOTAT but still failing to solve the item. On the backdrop of these results, we discuss implications and future potentials of log-file analyses in educational large-scale assessments for researchers, teachers, and policy makers.

Prevention of DDoS and Brute Force Attacks on Web Log Files using Combination of Genetic Algorithm and Feed forward Back Propagation Neural Network

World Wide Web has become an ultimate source of information. Traditional services such as banking, education, medicine, defence, and transportation are being presented by web applications. Whenever the users make use of any web application, all the activities of the users get automatically get appended into the web log files. The web log file data helps the website owners in number of ways such as customization of web content, pre-fetching and caching, E-commerce, etc. However, the web log file data is exposed to number of attacks. In this paper, a new technique has been proposed to prevent the log file data from the two most common attacks: Brute force attack and DDoS attack.

Provenance metadata gathering and cataloguing of EFIT++ code execution

Journal publications, as the final product of research activity, are the result of an extensive complex modeling and data analysis effort. It is of paramount importance, therefore, to capture the origins and derivation of the published data in order to achieve high levels of scientific reproducibility, transparency, internal and external data reuse and dissemination. The consequence of the modern research paradigm is that high performance computing and data management systems, together with metadata cataloguing, have become crucial elements within the nuclear fusion scientific data lifecycle. This paper describes an approach to the task of automatically gathering and cataloguing provenance metadata, currently under development and testing at Culham Center for Fusion Energy. The approach is being applied to a machine-agnostic code that calculates the axisymmetric equilibrium force balance in tokamaks, EFIT++, as a proof of principle test. The proposed approach avoids any code instrumentation or modification. It is based on the observation and monitoring of input preparation, workflow and code execution, system calls, log file data collection and interaction with the version control system. Pre-processing, post-processing, and data export and storage are monitored during the code runtime. Input data signals are captured using a data distribution platform called IDAM. The final objective of the catalogue is to create a complete description of the modeling activity, including user comments, and the relationship between data output, the main experimental database and the execution environment. For an intershot or post-pulse analysis (∼1000 time slices, 65×65 grid, mpi execution n=8 cores) of a typical MAST pulse, the overhead in the code runtime caused by the Provenance Metadata Gathering System is less than 10%, the metadata/data size ratio is about ∼20%, which we consider to be reasonable according to the present literature. A visualization interface based on Gephi for catalogue interrogation, will be presented.

A Novel Approach using XMI Log Generation Cryptography and Mining Techniques for to Enhance Security of Data

Security is the major issue in IT sector because data of an organization is an important asset for them. The information can be there client or customer contacts or it may be the information about their accounts, in short that information is very confidential information that they never want to disclose. But because of security attacks an attacker i.e. Hackers can access this information. Securities are required for network data and the database data. The all information is stores in the databases and the log of this information is also generated by the server. In the log files all information of users is stored, so the security of the log files is required. The log contains the all information of the users such as IP address, Mac address, Session time, Operating System etc. The hacker can hack the log and steal all the users’ information and edit it and upload on the server to avoid intrusion detection. So, in this research work proposes an idea for to prevent these types of attacks of the hacker. This work proposes the security of log files with the XMI file because the XMI file is a secured file. Retrieving the contents of XMI file is not easy because the XMI file contains the very big library of the java named XMI.Java. The log file data converting it into XMI file then encrypt the XMI file data again; it provides the more security of data. This makes the information more secure.

Predicting the Users&apos; Navigation Patterns in Web, using Weighted Association Rules and Users&apos; Navigation Information

World Wide Web is developing in a chaotic and unfocused process, and this process has resulted in production of documents which are linked with each other, and which are not logically organized. Therefore, the aim of recommender systems is guiding users to find their favorite resources and meet their needs, by using the information obtained from the previous users’ interactions. In this paper, to predict the users’ navigation pattern with high precision, a hybrid algorithm of FCM fuzzy clustering techniques, weighted association rules, and fuzzy systems are presented. This algorithm is implemented in two phases, namely offline and online phases. In offline phase, using the recorded data in log file of the web server, the users’ navigation patterns are extracted. In online phase, the recommender system suggests, as the initial proposed set, a list of the current user’s favorite webpages which he/she has not visited yet. Then it expands this set using HITS algorithm so that the new webpages which have recently been added to the website have the chance to be present in the list of the proposed webpages. The results of the simulation in real-world data indicate the higher efficiency of the proposed algorithm in terms of precision and coverage comparing to other algorithms.

Process Oriented Data Virtualization Design Model for Business Processes Evaluation(PODVDM) Research in Progress

During process enactment in the business process management (BPM) lifecycle, information collected on execution plans are stored in the form of log files and database tables by using information systems (IS). In the past decade, a new approach based on the applications of Business Intelligence (BI) in business process management has emerged. The approach implements process-oriented data warehouse and mining techniques. However, the main issue is providing the right information at the right time to facilitate process evaluation that can be used for performance analysis and improve business process. Existing techniques have limitations, including huge data in database log files, performance of Process Warehouse (PW), which is highly dependent on specific design), complexity of PW design, lack of convergence between business processes and PW specifications, and the need for real data during process evaluation stage. Objects such as processes, storage, and data repositories can be virtualized to address these limitations. The main aim of this study is to propose a process-oriented data virtualization design model for process evaluation in BPM. The model will be validated through expert reviews and prototype development as well as through a case study. In this paper, we describe the research motivation, questions, approach, and methodology related to addressing the described limitations by designing a model for evaluation in business processes using the Data Virtualization technique.

IJIREEICE - Electrical, Electronics, Instrumentation and Control

Authentication based on biometrics is being applied to control physical access to high-security facilities. Recently, with the recent rapid growth of information system technologies, applications for accessing databases or business workflow systems have begun to use biometrics such as hand geometry, palm, iris, fingerprint etc. As the integrity of data is important, We have to guarantee the integrity of the registered biometric data. This paper presents an identification-verification biometric system based on the combination of hand geometry features. The system is designed in NI Vision Assistant 2009. Such approach increased both detection and recognition efficiency of our palm and finger print biometrics system. The proposed system is reliable and user friendly as it is developed in Vision Assistant software. The availability of data log files in Vision Assistant and LabVIEW makes it one of the most useful component as a database.

Phantomless patient-specific TomoTherapy QA via delivery performance monitoring and a secondary Monte Carlo dose calculation.

To describe the validation and implementation of a novel quality assurance (QA) system for TomoTherapy using a Monte Carlo (MC)-based secondary dose calculation and CT detector-based multileaf collimator (MLC) leaf opening time measurement QA verification. This system is capable of detecting plan transfer and delivery errors and evaluating the dosimetric impact of those errors. The authors' QA process, MCLogQA, utilizes an independent pretreatment MC secondary dose calculation and postdelivery TomoTherapy exit detector-based MLC sinogram comparison and log file examination to confirm accurate dose calculation, accurate dose delivery, and to verify machine performance. MC radiation transport simulations are performed to estimate patient dose utilizing prestored treatment machine-specific phase-space information, the patient's planning CT, and MLC sinogram data. Sinogram data are generated from both the treatment planning system (MC_TPS) and from beam delivery log files (MC_Log). TomoTherapy treatment planning dose (DTPS) is compared with DMC_TPS and DMC_Log via dose-volume metrics and mean region of interest dose statistics. For validation, in-phantom ionization chamber dose measurements (DIC) for ten sample patient plans are compared with the computed values. Dose comparisons to in-phantom ion chamber measurements validate the capability of the MCLogQA method to detect delivery errors. DMC_Log agreed with DIC within 1%, while DTPS values varied by 2%-5% compared to DIC. The authors demonstrated that TomoTherapy treatments can be vulnerable to MLC deviations and interfraction output variations during treatment delivery. Interfractional Linac output variations for each patient were approximately 2% and average output was 1%-1.5% below the gold standard. While average MLC leaf opening time error from patient to patient varied from -0.6% to 1.6%, the MLC leaf errors varied little between fractions for the same patient plan, excluding one patient. MCLogQA is a new TomoTherapy QA process that validates the planned dose before delivery and analyzes the delivered dose using the treatment exit detector and log file data. The MCLogQA procedure is an effective and efficient alternative to traditional phantom-based TomoTherapy plan-specific QA because it allows for comprehensive 3D dose verification, accounts for tissue heterogeneity, uses patient CT density tables, reduces total QA time, and provides for a comprehensive QA methodology for each treatment fraction.

Connected play: tweens in a virtual world

Millions of children visit virtual worlds every day. In such virtual play spaces as Habbo Hotel, Toontown, and Whyville, kids chat with friends from school, meet new people, construct avatars, and earn and spend virtual currency. In Connected Play, Yasmin Kafai and Deborah Fields investigate what happens when kids play in virtual worlds, how this matters for their offline lives, and what this means for the design of educational opportunities in digital worlds. Play is fundamentally important for kids' development, but, Kafai and Fields argue, to understand play in virtual worlds, we need to connect concerns of development and culture with those of digital media and learning. Kafai and Fields do this through a detailed study of kids' play in Whyville, a massive, informal virtual world with educational content for tween players. Combining ethnographic accounts with analysis of logfile data, they present rich portraits and overviews of how kids learn to play in a digital domain, developing certain technological competencies; how kids learn to play well -- responsibly, respectfully, and safely; and how kids learn to play creatively, creating content that becomes a part of the virtual world itself.

Poster — Thur Eve — 27: Flattening Filter Free VMAT Quality Assurance: Dose Rate Considerations for Detector Response

Introduction:Radiation detector responses can be affected by dose rate. Due to higher dose per pulse and wider range of mu rates in FFF beams, detector responses should be characterized prior to implementation of QA protocols for FFF beams. During VMAT delivery, the MU rate may also vary dramatically within a treatment fraction. This study looks at the dose per pulse variation throughout a 3D volume for typical VMAT plans and the response characteristics for a variety of detectors, and makes recommendations on the design of QA protocols for FFF VMAT QA.Materials and Methods:Linac log file data and a simplified dose calculation algorithm are used to calculate dose per pulse for a variety of clinical VMAT plans, on a voxel by voxel basis, as a function of time in a cylindrical phantom. Diode and ion chamber array responses are characterized over the relevant range of dose per pulse and dose rate.Results:Dose per pulse ranges from &lt;0.1 mGy/pulse to 1.5 mGy/pulse in a typical VMAT treatment delivery using the 10XFFF beam. Diode detector arrays demonstrate increased sensitivity to dose (+./− 3%) with increasing dose per pulse over this range. Ion chamber arrays demonstrate decreased sensitivity to dose (+/− 1%) with increasing dose rate over this range.Conclusions:QA protocols should be designed taking into consideration inherent changes in detector sensitivity with dose rate. Neglecting to account for changes in detector response with dose per pulse can lead to skewed QA results.