255,978 research outputs found
The SIKS/BiGGrid Big Data Tutorial
The School for Information and Knowledge Systems SIKS and the Dutch e-science grid BiG Grid organized a new two-day tutorial on Big Data at the University of Twente on 30 November and 1 December 2011, just preceding the Dutch-Belgian Database Day. The tutorial is on top of some exciting new developments in large-scale data processing and data centers, initiated by Google, and followed by many others such as Yahoo, Amazon, Microsoft, and Facebook. The course teaches how to process terabytes of data on large clusters, and discusses several core computer science topics adapted for big data, such as new file systems (Google File System and Hadoop FS), new programming paradigms (MapReduce), new programming languages and query languages (Sawzall, Pig Latin), and new 'noSQL' databases (BigTable, Cassandra and Dynamo)
Transferring Google Earth observations to GIS-software : example from gully erosion study
High-resolution images available on Google Earth are increasingly being consulted in geographic studies. However, most studies limit themselves to visualizations or on-screen measurements. Google Earth allows users to create points, lines, and polygons on-screen, which can be saved as Keyhole Markup Language (KML) files. Here, the use of R statistics freeware is proposed to easily convert these files to the shapefile format [or .shp file format'], which can be loaded into Geographic Information System (GIS) software (ESRI ArcGIS 9 in our example). The geospatial data integration in GIS strongly increases the analysis possibilities
Analyzing Android Browser Apps for file:// Vulnerabilities
Securing browsers in mobile devices is very challenging, because these
browser apps usually provide browsing services to other apps in the same
device. A malicious app installed in a device can potentially obtain sensitive
information through a browser app. In this paper, we identify four types of
attacks in Android, collectively known as FileCross, that exploits the
vulnerable file:// to obtain users' private files, such as cookies, bookmarks,
and browsing histories. We design an automated system to dynamically test 115
browser apps collected from Google Play and find that 64 of them are vulnerable
to the attacks. Among them are the popular Firefox, Baidu and Maxthon browsers,
and the more application-specific ones, including UC Browser HD for tablet
users, Wikipedia Browser, and Kids Safe Browser. A detailed analysis of these
browsers further shows that 26 browsers (23%) expose their browsing interfaces
unintentionally. In response to our reports, the developers concerned promptly
patched their browsers by forbidding file:// access to private file zones,
disabling JavaScript execution in file:// URLs, or even blocking external
file:// URLs. We employ the same system to validate the ten patches received
from the developers and find one still failing to block the vulnerability.Comment: The paper has been accepted by ISC'14 as a regular paper (see
https://daoyuan14.github.io/). This is a Technical Report version for
referenc
Web Browser Private Mode Forensics Analysis
To maintain privacy of the end consumers the browser vendors
provide a very good feature on the browser called the Private Mode . As
per the browser vendors, the Private Mode ensures Cookies, Temporary
Internet Files, Webpage history, Form data and passwords, Anti-phishing
cache, Address bar and search AutoComplete, Automatic Crash Restore
(ACR) and Document Object Model (DOM) storage information is not
stored on the system [45].
To put to test the browser vendors claim, I had setup a test to confirm the
claims. During the first test the file system was monitored for all reads
and writes. On the second test the image of the RAM was taken after the
browser was used in private mode. The image was analyzed to check if the
RAM contained any data related to the user browsing. The browsers chosen
to perform this test were: Internet Explorer, Firefox, Google Chrome and
Safari.
During the file system monitoring analysis for the browsers in private mode
it was found that Google Chrome and Firefox didn\u27t write any data on the
file system. Safari wrote data on just a single file called WebpageIcons.db .
Internet Explorer wrote browsing data on the file system and then deleted
it. This data can be recovered using any recovery tool such as Recuva.
During the memory dump based analysis for the browsers in private mode,
it was found that browser data was recoverable for all the browsers.
Therefore from data privacy perspective Google Chrome and Firefox are
safer to use than Safari and Internet Explorer
ObliviSync: Practical Oblivious File Backup and Synchronization
Oblivious RAM (ORAM) protocols are powerful techniques that hide a client's
data as well as access patterns from untrusted service providers. We present an
oblivious cloud storage system, ObliviSync, that specifically targets one of
the most widely-used personal cloud storage paradigms: synchronization and
backup services, popular examples of which are Dropbox, iCloud Drive, and
Google Drive. This setting provides a unique opportunity because the above
privacy properties can be achieved with a simpler form of ORAM called
write-only ORAM, which allows for dramatically increased efficiency compared to
related work. Our solution is asymptotically optimal and practically efficient,
with a small constant overhead of approximately 4x compared with non-private
file storage, depending only on the total data size and parameters chosen
according to the usage rate, and not on the number or size of individual files.
Our construction also offers protection against timing-channel attacks, which
has not been previously considered in ORAM protocols. We built and evaluated a
full implementation of ObliviSync that supports multiple simultaneous read-only
clients and a single concurrent read/write client whose edits automatically and
seamlessly propagate to the readers. We show that our system functions under
high work loads, with realistic file size distributions, and with small
additional latency (as compared to a baseline encrypted file system) when
paired with Dropbox as the synchronization service.Comment: 15 pages. Accepted to NDSS 201
Keemei: cloud-based validation of tabular bioinformatics file formats in Google Sheets.
BackgroundBioinformatics software often requires human-generated tabular text files as input and has specific requirements for how those data are formatted. Users frequently manage these data in spreadsheet programs, which is convenient for researchers who are compiling the requisite information because the spreadsheet programs can easily be used on different platforms including laptops and tablets, and because they provide a familiar interface. It is increasingly common for many different researchers to be involved in compiling these data, including study coordinators, clinicians, lab technicians and bioinformaticians. As a result, many research groups are shifting toward using cloud-based spreadsheet programs, such as Google Sheets, which support the concurrent editing of a single spreadsheet by different users working on different platforms. Most of the researchers who enter data are not familiar with the formatting requirements of the bioinformatics programs that will be used, so validating and correcting file formats is often a bottleneck prior to beginning bioinformatics analysis.Main textWe present Keemei, a Google Sheets Add-on, for validating tabular files used in bioinformatics analyses. Keemei is available free of charge from Google's Chrome Web Store. Keemei can be installed and run on any web browser supported by Google Sheets. Keemei currently supports the validation of two widely used tabular bioinformatics formats, the Quantitative Insights into Microbial Ecology (QIIME) sample metadata mapping file format and the Spatially Referenced Genetic Data (SRGD) format, but is designed to easily support the addition of others.ConclusionsKeemei will save researchers time and frustration by providing a convenient interface for tabular bioinformatics file format validation. By allowing everyone involved with data entry for a project to easily validate their data, it will reduce the validation and formatting bottlenecks that are commonly encountered when human-generated data files are first used with a bioinformatics system. Simplifying the validation of essential tabular data files, such as sample metadata, will reduce common errors and thereby improve the quality and reliability of research outcomes
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Production of semi real time media-GIS contents using MODIS imagery
[Abstract]:
Delivering environmental disaster information, swiftly, attractively, meaningfully, and accurately, to public is becoming a competitive task among spatial data visualizing experts. Basically, the data visualization process has to follow basics of spatial data visualization to maintain the academic quality and the spatial accuracy of the content. Here, âMedia-GISâ, can be promoted as a one of the latest sub-forms of GIS, which targets mass media. Under Media-GIS, âPresentâ or the fist component of three roles of data visualization takes the major workload compare to other two, âAnalysisâ and âExploreâ. When present contents, optimizing the main graphical variables like, size, value, texture, hue, orientation, and shape, is vital with regard to the target market (age group, social group) and the medium (print, TV, WEB, mobile). This study emphasizes on application of freely available MODIS true colour images to produce near real time contents on environmental disasters, while minimizing the production cost. With the brake of first news of a significant environmental disaster, relevant MODIS (250m) images can be extracted in GeoTIFF and KLM (Keyhole Markup Language) formats from MODIS website. This original KML file can be overlayed on Google Earth, to collect more spatial information of the disaster site. Then, in ArcGIS environment, GeoTIFF file can be transferred into Photoshop for production of the graphics of the target spot. This media-friendly Photoshop file can be used as an independent content without geo-references or imported into ArcGIS to convert into KLM format, which has geo-references. The KLM file, which is graphically enhanced content with extra information on environmental disaster, can be used in TV and WEB through Google Earth. Also, sub productions can be directed into print and mobile contents. If the data processing can be automated, system will be able to produce media contents in a faster manner. A case study on the recent undersea oil spill occurred in Gulf of Mexico included in the report to highlight main aspects discussed in the methodology
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