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iSAX 2.0: Indexing and Mining One Billion Time Series; Database Cracking
 
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iSAX 2.0: Indexing and Mining One Billion Time Series abstract -------- There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to index and mine very large collections of time series. Examples of such applications come from astronomy, biology, the web, and other domains. It is not unusual for these applications to involve numbers of time series in the order of hundreds of millions to billions. In this paper, we describe iSAX 2.0, a data structure designed for indexing and mining truly massive collections of time series. We show that the main bottleneck in mining such massive datasets is the time taken to build the index, and we thus introduce a novel bulk loading mechanism, the first of this kind specifically tailored to a time series index. We show how our method allows mining on datasets that would otherwise be completely untenable, including the first published experiments to index one billion time series, and experiments in mining massive data from domains as diverse as entomology, DNA and web-scale image collections. Database Cracking and the Path Towards Auto-tuning Database Kernels ABSTRACT: Database cracking targets dynamic and exploratory environments where there is no sufficient workload knowledge and idle time to invest in physical design preparations and tuning. With DB cracking indexes are built incrementally, adaptively and on demand; each query is seen as an advice on how data should be stored. With each incoming query, data is reorganized on-the-fly as part of the query operators, while future queries exploit and continuously enhance this knowledge. Autonomously, adaptively and without any external human administration, the system quickly adapts to a new workload and reaches optimal performance when the workload stabilizes. We will talk about the basics of DB cracking including selection cracking, partial and sideways cracking and updates. We will also talk about important open and on going research issues such as disk based cracking, concurrency control and integration of cracking with offline and online index analysis.
Views: 326 Microsoft Research
SAXually Explicit Images: Data Mining Large Shape Databases
 
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Google TechTalks May 12, 2006 Eamonn Keogh ABSTRACT The problem of indexing large collections of time series and images has received much attention in the last decade, however we argue that there is potentially great untapped utility in data mining such collections. Consider the following two concrete examples of problems in data mining. Motif Discovery (duplication detection): Given a large repository of time series or images, find approximately repeated patterns/images. Discord Discovery: Given a large repository of time series or images, find the most unusual time series/image. As we will show, both these problems have applications in fields as diverse as anthropology, crime prevention, zoology and entertainment. Both problems are trivial to solve given time quadratic in the number of objects, but only a linear time solution is tractable for realistic problems. In this talk we will show how a symbolic representation of the data call SAX (Symbolic Aggregate ApproXimation) allows fast, scalable solutions to these problems. Google engEDU
Views: 4325 GoogleTalksArchive
SAXually Explicit Images: Data Mining Large Shape Databases
 
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Google TechTalks May 12, 2006 Eamonn Keogh ABSTRACT The problem of indexing large collections of time series and images has received much attention in the last decade, however we argue that there is potentially great untapped utility in data mining such collections. Consider the following two concrete examples of problems in data mining. Motif Discovery (duplication detection): Given a large repository of time series or images, find approximately repeated patterns/images. Discord Discovery: Given a large repository of time series or images, find the most unusual time series/image. As we will show, both these problems have applications in fields as diverse as anthropology, crime...
Views: 4607 Google
Indexing for Time Series
 
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Recorded with http://screencast-o-matic.com
Views: 88 Andrew Ardern
SAXually Explicit Images: Data Mining Large Shape Databases
 
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Google TechTalks May 12, 2006 Eamonn Keogh ABSTRACT The problem of indexing large collections of time series and images has received much attention in the last decade, however we argue that there is potentially great untapped utility in data mining such collections. Consider the following two concrete examples of problems in data mining. Motif Discovery (duplication detection): Given a large repository of time series or images, find approximately repeated patterns/images. Discord Discovery: Given a large repository of time series or images, find the most unusual time series/image. As we will show, both these problems have applications in fields as diverse as anthropology, crime...
Views: 1479 GoogleTechTalks
Query Workloads for Data Series Indexes
 
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Auhtors: Kostas Zoumpatianos, Yin Lou, Themis Palpanas, Johannes Gehrke Abstract: Data series are a prevalent data type that has attracted lots of interest in recent years. Most of the research has focused on how to efficiently support similarity or nearest neighbor queries over large data series collections (an important data mining task), and several data series summarization and indexing methods have been proposed in order to solve this problem. Nevertheless, up to this point very little attention has been paid to properly evaluating such index structures, with most previous work relying solely on randomly selected data series to use as queries (with/without adding noise). In this work, we show that random workloads are inherently not suitable for the task at hand and we argue that there is a need for carefully generating a query workload. We define measures that capture the characteristics of queries, and we propose a method for generating workloads with the desired properties, that is, effectively evaluating and comparing data series summarizations and indexes. In our experimental evaluation, with carefully controlled query workloads, we shed light on key factors affecting the performance of nearest neighbor search in large data series collections. ACM DL: http://dl.acm.org/citation.cfm?id=2783382 DOI: http://dx.doi.org/10.1145/2783258.2783382
Seminar@SystemX - Themis Palpanas - Data Series Management
 
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There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to index and mine very large collections of sequences, or data series. Examples of such applications come from social media analytics and internet service providers, as well as from a multitude of scientific domains. It is not unusual for these applications to involve numbers of data series in the order of hundreds of millions to billions, which are often times not analyzed in their full detail due to their sheer size. However, no existing data management solution (such as relational databases, column stores, array databases, and time series management systems) can offer native support for sequences and the corresponding operators necessary for complex analytics. In this talk, we argue for the need to study the theory and foundations for sequence management of big data sequences, and to build corresponding systems that will enable scalable management and analysis of very large sequence collections. We describe recent efforts in designing techniques for indexing and mining truly massive collections of data series that will enable scientists to easily analyze their data. We discuss novel techniques that adaptively create data series indexes, allowing users to correctly answer queries before the indexing task is finished. Finally, we present our vision for the future in big sequence management research, including the promising directions in terms of storage, distributed processing, and query benchmarks.
Views: 63 IRT SystemX
Shaplets, Motifs and Discords: A set of Primitives for Mining Massive Time Series and Image Archives
 
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The past decade has seen tremendous interest in mining of time series and shape datasets, as such data can be found in domains as diverse as entertainment, finance, medicine and astronomy. However, much of this work has focused on toy problems, with a few thousand objects. In recent years, our research group has made an effort to address the problems of classification, clustering, query-by-content, motif discovery, and outlier detection on truly massive datasets, with 100 million-plus objects. In this talk we will summarize our research findings over the last two years, and show that a small set of primitives, shaplets, motifs and discords, allow us to solve essentially all problems in shape/time series data mining with efficient, effective and interpretable results. We will demonstrate the utility of our ideas, with case studies in anthropology, astronomy, entomology, historical manuscript annotation and medicine.
Views: 479 Microsoft Research
Time Series Forecasting Theory | AR, MA, ARMA, ARIMA | Data Science
 
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In this video you will learn the theory of Time Series Forecasting. You will what is univariate time series analysis, AR, MA, ARMA & ARIMA modelling and how to use these models to do forecast. This will also help you learn ARCH, Garch, ECM Model & Panel data models. For training, consulting or help Contact : [email protected] For Study Packs : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 296937 Analytics University
Data Mining using the Excel Data Mining Addin
 
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The Excel Data Mining Addin can be used to build predictive models such as Decisions Trees within Excel. The Excel Data Mining Addin sends data to SQL Server Analysis Services (SSAS) where the models are built. The completed model is then rendered within Excel. I also have a comprehensive 60 minute T-SQL course available at Udemy : https://www.udemy.com/t-sql-for-data-analysts/?couponCode=ANALYTICS50%25OFF
Views: 71491 Steve Fox
Mining Temporal Patterns in Time Interval- Based Data | Final Year Projects 2016
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 49 myproject bazaar
Database Clustering Tutorial 1 - Intro to Database Clustering
 
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Read the Blog: https://www.calebcurry.com/blogs/database-clustering/intro-to-database-clustering Get ClusterControl: http://bit.ly/ClusterControl In this video we are going to be discussing database clustering and how to manage database clusters with ClusterControl. Database clustering is when you have multiple computers working together that are all used to store your data. There are four primary reasons you should consider clustering. Data redundancy, Load balancing (scalability) High availability. Monitoring and Automation That is an intro to a few of the reasons having a cluster is a good idea. Obviously, not everyone needs a cluster. A cluster can be overkill. But the best way to know is to learn more about them, so I’ll see you in the next video! ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Support me! http://www.patreon.com/calebcurry Subscribe to my newsletter: http://eepurl.com/-8qtH Donate!: http://bit.ly/DonateCTVM2. ~~~~~~~~~~~~~~~Additional Links~~~~~~~~~~~~~~~ More content: http://CalebCurry.com Facebook: http://www.facebook.com/CalebTheVideoMaker Google+: https://plus.google.com/+CalebTheVideoMaker2 Twitter: http://twitter.com/calebCurry Amazing Web Hosting - https://www.dreamhost.com/r.cgi?1487063 (The best web hosting for a cheap price!)
Views: 12936 Caleb Curry
Comparing Series
 
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(Index: https://www.stat.auckland.ac.nz/~wild/wildaboutstatistics/ ) It is often interesting and useful to compare several series in terms of trend and seasonal patterns. How do the trends compare? How big are the seasonal effects for one series compared to another? Do they all behave in the same way at the same times? What oddities stand out in the plots? After you’ve watched this video, you should be able to answer these questions •When we are plotting several related series so that we can compare the patterns in them, what are the strengths and the weaknesses of a plot that puts all of the series on the same graph? •When we are plotting several related series so that we can compare the patterns in them, what are the strengths and the weaknesses of a plot that puts all of the series on their own separate graphs? •What types of feature of each series can we compare using the iNZight graphs for comparing series?
Views: 3656 Wild About Statistics
Tap the Hidden Value of Time-Series Data With SQL
 
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The more you know, and the faster you can be aware of what's happening in your facility, the more competitive you'll be in your industry. SQL relational databases make time-series data from the plant floor accessible to the entire enterprise. Combining the power of SQL with your SCADA system facilitates more people to ask more important questions about your data -- anyone from the plant operator to the CEO. Getting answers to questions you have about your facility in real time can result in immediate, impactful, potentially revolutionary insights into what's happening in your company, right now. In this webinar, you'll discover the following strategies to boost your company's knowledge: 1. Why SQL databases are a better choice over process historians. 2. Get true "real-time" enterprise data. 3. How to collaborate with the entire enterprise, with ease.
Fred Moyer: Solving the Technical Challenges of Time Series Databases at Scale
 
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Read the full blog post here - https://www.heavybit.com/library/blog/sf-metrics-opentracing-metrics-and-time-series-databases-at-scale/ Time series databases are optimized for handling sets of data indexed by time. Aspects of data storage, data safety, and the iops problem are challenges that all TSDBs face at scale. In this talk, Fred outlines how IRONdb solves these technical problems, or avoids them entirely. IRONdb is a commercial time series database developed by Circonus, and is a Graphite compatible drop in replacement for Whisper. For more developer focused content, visit https://www.heavybit.com/library
Views: 687 Heavybit
Distinguished Lecturer Series - Christos Faloutsos: "Mining Large Graphs"
 
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DISTINGUISHED LECTURER SERIES Mining Large Graphs Dr. Christos Faloutsos Carnegie Mellon University Recorded on April 16, 2015 11:00 a.m., 1000 SEO Building Abstract: Given a large graph, like who-calls-whom, or who-likes-whom, what behavior is normal and what should be surprising, possibly due to fraudulent activity? How do graphs evolve over time? We focus on these topics: (a) Anomaly detection in large static graphs and (b) Patterns and anomalies in large time-evolving graphs. For the first, we present a list of static and temporal laws, including advances patterns like 'eigenspokes'; we show how to use them to spot suspicious activities, in on-line buyer-and-seller settings, in FaceBook, in twitter-like networks. For the second, we show how to handle time-evolving graphs as tensors, how to handle large tensors in map-reduce environments, as well as some discoveries such settings. We conclude with some open research questions for graph mining. Bio: Christos Faloutsos is a Professor at Carnegie Mellon University. He has received the Presidential Young Investigator Award by the National Science Foundation (1989), the Research Contributions Award in ICDM 2006, the SIGKDD Innovations Award (2010), twenty “best paper” awards(including two “test of time” awards), and four teaching awards. Five of his advisees have attracted KDD or SCS dissertation awards. He is an ACM Fellow, he has served as a member of the executive committee of SIGKDD; he has published over 300 refereed articles, 17 book chapters and two monographs. He holds eight patents and he has given over 35 tutorials and over 15 invited distinguished lectures. His research interests include data mining for graphs and streams, fractals, database performance, and indexing for multimedia and bio-informatics data. Host: Dr. Bing Liu
Data Cubes for Large Scale Data Analytics
 
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Recent work by WGISS members has been fleshing out the concept of Data Cubes to enable analysis of large Earth Observation data sets. Please join us as Rob Woodcock of CSIRO (Australia) and Brian Killough of the CEOS System Engineering Office provide an introduction to Data Cubes. Rob will set the stage for Data Cubes with user needs, key features and basic high-level architecture, followed by Brian to talk about some more of the inner workings of Data Cubes.
Exploring GIS: Spatial data representation
 
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An overview of how the real world is decomposed and stored digitally in the computer, what are spatial data models, specifying the vector data model, review of the raster data model, and map symbolizations.
Views: 16479 GIS VideosTV
IDA2014 - Symbolic Time Series Representation for Stream Data Processing
 
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Full title: Symbolic Time Series Representation for Stream Data Processing By Jakub Ševcech and Mária Bieliková
Data Mining in SQL Server Analysis Services
 
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Presenter: Brian Knight
Views: 96249 PASStv
What is Metadata?
 
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WHAT IS METADATA?: This short video by John Bond of Riverwinds Consulting discusses metadata in scholarly publishing. MORE VIDEOS on metadata can be found at: https://www.youtube.com/playlist?list=PLqkE49N6nq3hVVC96f9I5YUnIZGLSLs6N FIND OUT more about John Bond and his publishing consulting practice at www.RiverwindsConsulting.com JOHN'S NEW BOOK is “The Request for Proposal in Publishing: Managing the RFP Process” To find out more about the book: https://www.riverwindsconsulting.com/rfps Buy it at Amazon: https://www.amazon.com/Request-Proposal-Publishing-Managing-Process-ebook/dp/B071W7MBLM/ref=sr_1_1?s=books&ie=UTF8&qid=1497619963&sr=1-1&keywords=john+bond+rfps SEND IDEAS for John to discuss on Publishing Defined. Email him at [email protected] or see http://www.PublishingDefined.com CONNECT Twitter: https://twitter.com/JohnHBond LinkedIn: https://www.linkedin.com/in/johnbondnj Google+: https://plus.google.com/u/0/113338584717955505192 Goodreads: https://www.goodreads.com/user/show/51052703-john-bond YouTube: https://www.youtube.com/c/JohnBond BOOKS by John Bond: The Story of You: http://www.booksbyjohnbond.com/the-story-of-you/about-the-book/ You Can Write and Publish a Book: http://www.booksbyjohnbond.com/you-can-write-and-publish-a-book/about-the-book/ TRANSCRIPT Hi there. I am John Bond from Riverwinds Consulting and this is Publishing Defined. Today I am going to discuss metadata in academic publishing. In a recent survey, improving metadata was the top business priority for scholarly publishing executives this year. Metadata, as we know, is data about data. It is more than just keywords. High quality metadata is essential for properly structuring a website or digital product and most importantly, for discoverability by search engines. It provides for the digital identification of content, and supports the archiving of this content. It is also essential for good business decisions for organizational growth and product development. Metadata can be divided into several types, some of which might include: Structural metadata, Descriptive metadata, Technical metadata, Administrative metadata, Rights metadata, and other categories. Whether discussing journals, books, databases, or other educational products, having your metadata house in order is essential. First, following commonly accepted XML tagging practices is a must. While a publisher may offer its content at its own website, ultimately the content is used and processed by many third parties and therefore complying with standards from CrossRef, PubMed, ORCID, Google Scholar, Amazon, and many others including other abstracting and indexing organizations, is essential. Using industry accepted tagging practices, will make the content much more accessible and increase sales. And well-structured metadata, in and of itself, may have commercial value, past the advertising value. Second, automating and streamlining internal metadata practices is an important task for the publishers’ leadership to be involved with and knowledgeable about, and not just delegate. Avoid homebrewing your metadata in-house with an individual or group of individuals. While this may seem economical, it will cost in the long run. Perhaps use metadata management software or, better yet, a metadata partner. Best of all is to have this software or partner be specific to scholarly publishing. The costs will outweigh the benefits. Ongoing changes to standards such as XML, JATS, ONIX, BISAC, the Dublin Core, and many others will tax the resources of even large organizations. Working with a metadata management partner will help the publisher stay up to date with these changes. Other benefits include: reducing staff time by not creating conflicting or duplicate metadata; the creation of higher quality metadata; reaching more readers/customers/partner;, adhering to best practices; potentially increasing sales; and more. An audit of your current and legacy products will show if there is room for improvement. What standards are being used and are they current versions are key factors to consider? Are all current partners satisfied with your products’ current metadata feed? As the world and publishing continues to embrace AI or artificial intelligence, the proper use of metadata will only become more important. Get involved and dive in, or engage an outside company to give an assessment of your current metadata status.........
Views: 427 John Bond
Import Data and Analyze with MATLAB
 
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Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 315763 APMonitor.com
8. Time Series Analysis I
 
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MIT 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 View the complete course: http://ocw.mit.edu/18-S096F13 Instructor: Peter Kempthorne This is the first of three lectures introducing the topic of time series analysis, describing stochastic processes by applying regression and stationarity models. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 152243 MIT OpenCourseWare
RINSE: Interactive Data Series Exploration
 
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URL: http://daslab.seas.harvard.edu/rinse People: Kostas Zoumpatianos (University of Trento), Stratos Idreos (Harvard University), Themis Palpanas (Paris Descartes University) Information: ------------------ Numerous applications continuously produce big amounts of data series, and in several time critical scenarios analysts need to be able to query these data as soon as they become available, which is not currently possible with the state-of-the-art indexing methods and for very large data series collections. We develop the first adaptive data series indexing mechanism, called ADS+, specifically tailored to solve the problem of indexing and querying very large data series collections. The main idea is that instead of building the complete index over the complete data set up-front and querying only later, we interactively and adaptively build parts of the index, only for the parts of the data on which the users pose queries. The net effect is that instead of waiting for extended periods of time for the index creation, users can immediately start exploring the data series. In this demonstration we present RINSE, a system that allows users to experience the benefits of ADS+ through an intuitive web interface. It allows them to explore large datasets and find patterns of interest, using nearest neighbor search. Users can either draw queries using a mouse or touch screen or they can select them from other data series collections. RINSE can scale to large data sizes, while drastically reducing the data to query delay: by the time state-of-the-art indexing techniques finish indexing 1 billion data series (and before answering even a single query), adaptive data series indexing can already answer $3*10^5$ queries.
Views: 727 Kostas Zoumpatianos
Information Visualization for Knowledge Discovery
 
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Information Visualization for Knowledge Discovery Ben Shneiderman [University of Maryland--College Park] Abstract: Interactive information visualization tools provide researchers with remarkable capabilities to support discovery. By combining powerful data mining methods with user-controlled interfaces, users are beginning to benefit from these potent telescopes for high-dimensional data. They can begin with an overview, zoom in on areas of interest, filter out unwanted items, and then click for details-on-demand. With careful design and efficient algorithms, the dynamic queries approach to data exploration can provide 100msec updates even for million-record databases. This talk will start by reviewing the growing commercial success stories such as www.spotfire.com, www.smartmoney.com/marketmap and www.hivegroup.com. Then it will cover recent research progress for visual exploration of large time series data applied to financial, medical, and genomic data (www.cs.umd.edu/hcil/timesearcher ). These strategies of unifying statistics with visualization are applied to electronic health records (www.cs.umd.edu/hcil/lifelines2) and social network data (www.cs.umd.edu/hcil/socialaction and www.codeplex.com/nodexl). Demonstrations will be shown. BEN SHNEIDERMAN is a Professor in the Department of Computer Science and Founding Director (1983-2000) of the Human-Computer Interaction Laboratory at the University of Maryland. He was elected as a Fellow of the Association for Computing (ACM) in 1997 and a Fellow of the American Association for the Advancement of Science (AAAS) in 2001. He received the ACM SIGCHI Lifetime Achievement Award in 2001. Ben is the author of "Designing the User Interface: Strategies for Effective Human-Computer Interaction" (5th ed. March 2009, forthcoming) http://www.awl.com/DTUI/. With S. Card and J. Mackinlay, he co-authored "Readings in Information Visualization: Using Vision to Think" (1999). With Ben Bederson he co-authored The Craft of Information Visualization (2003). His book Leonardos Laptop appeared in October 2002 (MIT Press) (http://mitpress.mit.edu/leonardoslaptop) and won the IEEE book award for Distinguished Literary Contribution.
Views: 23394 CITRIS
What is SIMILARITY SEARCH? What does SIMILARITY SEARCH mean? SIMILARITY SEARCH meaning & explanation
 
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What is SIMILARITY SEARCH? What does SIMILARITY SEARCH mean? SIMILARITY SEARCH meaning - SIMILARITY SEARCH definition - SIMILARITY SEARCH explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Similarity search is the most general term used for a range of mechanisms which share the principle of searching (typically, very large) spaces of objects where the only available comparator is the similarity between any pair of objects. This is becoming increasingly important in an age of large information repositories where the objects contained do not possess any natural order, for example large collections of images, sounds and other sophisticated digital objects. Nearest neighbor search and range queries are important subclasses of similarity search, and a number of solutions exist. Research in Similarity Search is dominated by the inherent problems of searching over complex objects. Such objects cause most known techniques to lose traction over large collections, and there are still many unsolved problems. Unfortunately, in many cases where similarity search is necessary, the objects are inherently complex. The most general approach to similarity search that allows construction of efficient index structures use the mathematical notion of metric space. A popular approach for similarity search is locality sensitive hashing – LSH. hashes input items so that similar items map to the same "buckets" in memory with high probability (the number of buckets being much smaller than the universe of possible input items). It is often applied in nearest neighbor search on large scale high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases.
Views: 336 The Audiopedia
Applying SparkSQL to Big Spatio Temporal Data Using GeoMesa -  Anthony Fox
 
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GeoMesa is an open-source toolkit for processing and analyzing spatio-temporal data, such as IoT and sensor-produced observations, at scale. It provides a consistent API for querying and analyzing data on top of distributed databases (e.g. HBase, Accumulo, Bigtable, Cassandra) and messaging networks (e.g. Kafka) to handle batch analysis of historical archives of data and low-latency processing of data in-stream.
Views: 1570 Databricks
How to Import Data, Copy Data from Excel to R: .csv & .txt Formats (R Tutorial 1.5)
 
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Learn how to import or copy data from excel (or other spreadsheets) into R using both comma-separated values and tab-delimited text file. You will learn to use "read.csv", "read.delim" and "read.table" commands along with "file.choose", "header", and "sep" arguments. This video is a tutorial for programming in R Statistical Software for beginners. You can access the dataset here: our website: http://www.statslectures.com/index.php/r-stats-videos-tutorials/getting-started-with-r/1-3-import-excel-data or here: Excel Data Used in This Video: http://bit.ly/1uyxR3O Excel Data Used in Subsequent Videos: https://bit.ly/LungCapDataxls Tab Delimited Text File Used in Subsequent Videos: https://bit.ly/LungCapData Here is a quick overview of the topics addressed in this video; click on time stamps to jump to a specific topic: 0:00:17 the two main file types for saving a data file 0:00:36 how to save a file in excel as a csv file ("comma-separated value") 0:01:10 how to open a comma-separated (.csv) data file into excel 0:01:20 how to open a comma-separated (.csv) data file into a text editor 0:01:36 how to import comma-separated (.csv) data file into R using "read.csv" command 0:01:44 how to access the help menu for different commands in R 0:02:04 how to use "file.choose" argument on "read.csv" command to specify the file location in R 0:02:31 how to use the "header" argument on "read.csv" command to let R know that data has headers or variable names 0:03:22 how to import comma-separated (.csv) data file into R using "read.table" command 0:03:38 how to use "file.choose" argument on "read.table" command to specify the file location in R 0:03:41 how to use the "header" argument on "read.table" command to let R know the data has headers or variable names 0:03:46 how to use the "sep" argument on "read.table" command to let R know how the data values are separated 0:04:10 how to save a file in excel as tab-delimited text file 0:04:50 how to open a tab-delimited (.txt) data file into a text editor 0:05:07 how to open a tab-delimited (.txt) data file into excel 0:05:20 how to import tab-delimited (.txt) data file into R using "read.delim" command 0:05:44 how to use "file.choose" argument on "read.delim" command to specify the file path in R 0:05:49 how to use the "header" argument on "read.delim" command to let R know that the data has headers or variable 0:06:06 how to import tab-delimited (.txt) data file into R using "read.table" command 0:06:20 how to use "file.choose" argument on "read.table" command to specify the file location 0:06:23 how to use the "header" argument on "read.table" command to let R know that the data has headers or variable names 0:06:27 how to use the "sep" argument on "read.table" command to let R know how the data values are separated
Views: 490265 MarinStatsLectures
Data Mining | Min-Max Normalization | Normal Distribution | Data Mining Algorithms
 
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Data Mining | Min-Max Normalization | Normal Distribution | Data Mining Algorithms *************************************************** python data science python machine learning data normalization nlp machine learning machine learning tutorial web crawler time series analysis natural language processing weka computer vision time series scrap decimals minmax scikit learn opencv python database normalization 1 python max python counterdecimal number python read csv fourier transform graph algorithm normalization fft matlab sklearn opencv normalisation decimal fourier matlab matrix matlab 3d plot matlab plot matlab mean pandas groupby preprocessor sklearn logistic regression matlab if matlab colors standardization axis matlab matlab function matlab colormap matlab array centre meaning feature meaning normal distribution Please Subscribe My Channel
Database Lesson #8 of 8 - Big Data, Data Warehouses, and Business Intelligence Systems
 
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Dr. Soper gives a lecture on big data, data warehouses, and business intelligence systems. Topics covered include big data, the NoSQL movement, structured storage, the MapReduce process, the Apache Cassandra data model, data warehouse concepts, multidimensional databases, business intelligence (BI) concepts, and data mining,
Views: 73094 Dr. Daniel Soper
GeoMesa as a Distributed Spatio-Temporal Database and Computational Framework
 
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by Jim Hughes Find more about GeoMesa at http://geomesa.org GeoMesa builds on the Hadoop and Accumulo ecosystem to scale up indexing billions of spatio-temporal data. This presentation will showcase and discuss some of GeoMesa's existing distributed computational capabilities such as K-nearest neighbor queries, and then move on to highlight relevant work by the fall 2014 Facebook Open Academy (FOA) students. The FOA students have created a Web Processing Service (WPS) process to get back aggregate time series data for an Extended Common Query Language (ECQL) query. Examples and illustrations will use the open Global Database of Events, Language, and Tone (GDELT) dataset. The conclusion will include ideas for future work in distributed database computation touching on leveraging Spark and Tez. This presentation will be of interest to data scientists, geospatial systems developers, and users of massive Spatio-Temporal datasets.
Views: 1823 Andrea Ross
Postgres with Apache Ignite:  Faster Transactions and Analytics
 
01:05:01
For the presentation slides, please visit: https://www.gridgain.com/resources/technical-presentations Join Fotios Filacouris, GridGain Solution Architect, as he discusses how you can supplement PostgreSQL with Apache Ignite. You'll learn: The strategic benefits of using Apache Ignite instead of Memcache, Redis®, GigaSpaces®, or Oracle Coherence™ How to overcome the limitations of the PostgreSQL architecture for big data analytics by leveraging the parallel distributed computing and ANSI SQL-99 capabilities of Apache Ignite How to use Apache Ignite as an advanced high-performance cache platform for hot data At the end of this webinar, you will understand how incorporating Apache Ignite into your architecture can empower dramatically faster analytics and transactions when augmenting your current PostgreSQL data architecture.
Views: 890 GridGain Systems
FAME database
 
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Information and Library Services Manager Andy Priestner describes the FAME database.
Views: 525 CJBSInfoLib
Grass Data Explorer -- Part 1/4: Visualisation of raster and vector layers
 
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The Grass Data Explorer QGIS plugin was designed to explore raster, vector and in particular time series data of a GRASS GIS location in a fast way. In this part a large raster layer and a vector layer are loaded from the GRASS spatial database into QGIS. Official repository: https://bitbucket.org/huhabla/grass-data-explorer Documentation: https://bitbucket.org/huhabla/grass-data-explorer/wiki/Home Part 1: https://youtu.be/Ub5OOuQdZAM Part 2: https://youtu.be/xxHt3jJbnYw Part 3: https://youtu.be/T4b03phnlrM Part 4: https://youtu.be/7ZsDMouKfnI Music: Aurea Carmina fromKevin MacLeod Creative Commons Attribution license https://creativecommons.org/licenses/by/4.0 http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1400006 http://incompetech.com/
Views: 254 Sören Gebbert
SAP HANA Academy - PAL: 93. Data Preparation - Partitioning [SPS 10]
 
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In this video tutorial, Philip Mugglestone explains how to access the partitioning functionality of the PAL via a SQL window function - a new capability introduced in SAP HANA SPS10. To access the code snippets used in the video series please visit https://github.com/saphanaacademy/PAL A video by the SAP HANA Academy.
Views: 1261 SAP HANA Academy
BOB'S BIG ROCKET - Bob's Mods Factorio - Part 103
 
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Let's play Factorio. In this series I will be playing Bob's Mods Factorio and trying to launch a rocket for every episode that we've done! So if you're having fun and liking the content then fire some likes, drop some comments and share it with your buddies! Download Factorio : http://www.factorio.com/order Bob's Big Rocket Playlist : https://www.youtube.com/playlist?list=PLifNPJsp2MOeTXznUdAhUBrJ0s0ns-bOD Patreon: http://www.patreon.com/Steejo Twitter : http://twitter.com/Steejo Twitch Tv : http://www.twitch.tv/steejo Mods: https://drive.google.com/open?id=0B436Viv_80QweEN4MmtsMElMWVk Advanced Logistics System, Autofill, Every Bob's Mods Mod, EvoGUI, FARL, Larger Inventory, Launch Control, Long Reach, MoreLight, RailTanker, Research Queue, RSO, SmallFixes, TheFatController, Tree Collision, Upgrade Planner, YARM. What is Factorio? Factorio is a game in which you build and maintain factories. You will be mining resources, researching technologies, building infrastructure, automating production and fighting enemies. Use your imagination to design your factory, combine simple elements into ingenious structures, apply management skills to keep it working and finally protect it from the creatures who don't really like you. For More Factorio Information visit: Factorio on Steam: http://store.steampowered.com/app/427520/ Factorio Official Website: http://www.factorio.com/ Factorio Official Trailer: https://youtu.be/9yDZM0diiYc Factorio Forums: http://factorioforums.com/forum/ Factorio Wiki: http://factorioforums.com/wiki/index.php?title=Main_Page Factorio is developed by Wube Software who were kind enough to share a game key with me.
Views: 1927 Steejo
011. Discovering Common Motifs in Mouse Cursor Movement Data - Дмитрий Лагун
 
51:04
Mouse cursor movements can provide valuable information on how users interact and engage with web documents. This interaction data is far richer than traditional click data, and can be used to improve evaluation and presentation of web information systems. Unfortunately, the diversity and complexity inherent in this interaction data make it more difficult to capture salient behavior characteristics through traditional feature engineering. To address this problem, we introduce a novel approach of automatically discovering frequent subsequences, or motifs, in mouse cursor movement data. In order to scale our approach to realistic datasets, we introduce novel optimizations for motif discovery, specifically designed for mining cursor movement data. We show that by encoding the motifs discovered from thousands of real web search sessions as features, enables significant improvements in important web search tasks. These results, complemented with visualization and qualitative analysis, demonstrate that our approach is able to automatically capture key characteristics of mouse cursor movement behavior, providing a valuable new tool for online user behavior analysis. In addition to the application of motifs to web mining, we demonstrate that similar technique can be successfully applied in medical domain for the task of predicting future decline of memory function and subsequent development of the Alzheimer Disease.
Data Structures: Crash Course Computer Science #14
 
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Today we’re going to talk about on how we organize the data we use on our devices. You might remember last episode we walked through some sorting algorithms, but skipped over how the information actually got there in the first place! And it is this ability to store and access information in a structured and meaningful way that is crucial to programming. From strings, pointers, and nodes, to heaps, trees, and stacks get ready for an ARRAY of new terminology and concepts. Ps. Have you had the chance to play the Grace Hopper game we made in episode 12. Check it out here! http://thoughtcafe.ca/hopper/ Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 291902 CrashCourse
Regunath Balasubramanian - Building tiered data stores using Aesop to bridge SQL and NoSQL systems
 
41:16
Large scale internet systems often use a combination of relational (SQL) and non-relational (NoSQL) data stores. Contrary to product claims, it is hard to find a single data store that meets common read-write patterns of on-line applications. Different databases try to optimize for specific workload patterns and data durability, consistency guarantees - use Memory buffer pools, Write-ahead logs, optimize for Flash storage etc. These data stores are not operated in isolation and need to share data and updates on it - for e.g. a high performance memory based KV data cache might need to be updated when data in the source-of-truth RDBMS or Columnar database changes. This talk discusses general approaches to Change Data Propagation and specific implementation details of Flipkart’s open-source project : Aesop, including some of its live deployments. It covers capabilities suitable for single node deployment and also scale to multi-node partitioned clusters that process data concurrently at high throughput. Aesop scales by partitioning the data stream and coordinates across subscription nodes using Zookeeper. It provides atleast-once delivery guarantees and timeline ordered data updates. Aesop is used at scale in business critical systems - the multi-tiered payments data store, the user wishlist system and streaming facts to data analysis platform. A number of upcoming adopters include the Promotions and Warehousing systems backend data stores. Aesop has been used successfully to move millions of data records between MySQL, HBase, Redis, Kafka and Elastic Search clusters. Aesop shares common design approach and technologies with Facebook Wormhole system Come attend this talk if you are evaluating data store(s) for your large scale service or are grappling with more immediate problems like cache invalidation.
Views: 554 HasGeek TV
BOB'S BIG ROCKET - Bob's Mods Factorio - Part 150
 
21:55
Let's play Factorio. In this series I will be playing Bob's Mods Factorio and trying to launch a rocket for every episode that we've done! So if you're having fun and liking the content then fire some likes, drop some comments and share it with your buddies! Download Factorio : http://www.factorio.com/order Bob's Big Rocket Playlist : https://www.youtube.com/playlist?list=PLifNPJsp2MOeTXznUdAhUBrJ0s0ns-bOD Patreon: http://www.patreon.com/Steejo Twitter : http://twitter.com/Steejo Twitch Tv : http://www.twitch.tv/steejo Mods: https://drive.google.com/open?id=0B436Viv_80QweEN4MmtsMElMWVk Advanced Logistics System, Autofill, Every Bob's Mods Mod, EvoGUI, FARL, Larger Inventory, Launch Control, Long Reach, MoreLight, RailTanker, Research Queue, RSO, SmallFixes, TheFatController, Tree Collision, Upgrade Planner, YARM. What is Factorio? Factorio is a game in which you build and maintain factories. You will be mining resources, researching technologies, building infrastructure, automating production and fighting enemies. Use your imagination to design your factory, combine simple elements into ingenious structures, apply management skills to keep it working and finally protect it from the creatures who don't really like you. For More Factorio Information visit: Factorio on Steam: http://store.steampowered.com/app/427520/ Factorio Official Website: http://www.factorio.com/ Factorio Official Trailer: https://youtu.be/9yDZM0diiYc Factorio Forums: http://factorioforums.com/forum/ Factorio Wiki: http://factorioforums.com/wiki/index.php?title=Main_Page Factorio is developed by Wube Software who were kind enough to share a game key with me.
Views: 1774 Steejo
B2B Big Data Challenges, Nick Mehta, Gainsight (Data Driven NYC / FirstMark Capital)
 
21:22
Nick Mehta, CEO at Gainsight, presented at FirstMark's Data Driven NYC on December 14, 2015. Mehta discussed the challenges of using Big Data at B2B companies. Gainsight helps companies use Big Data to grow faster, reduce churn, increase upsell opportunities and drive customer advocacy. Data Driven NYC is a monthly event covering Big Data and data-driven products and startups, hosted by Matt Turck, partner at FirstMark. FirstMark is an early stage venture capital firm based in New York City. Find out more about Data Driven NYC at http://datadrivennyc.com and FirstMark Capital at http://firstmarkcap.com.
Views: 423 Data Driven NYC
What is Spatial Data? by Gail Millin-Chalabi
 
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Spatial Data - what it is and how to use it. For more methods resources see: http://www.methods.manchester.ac.uk
Views: 2244 methodsMcr
Extremely Fast Decision Tree Mining for Evolving Data Streams
 
02:03
Extremely Fast Decision Tree Mining for Evolving Data Streams Albert Bifet (Telecom ParisTech) Jiajin Zhang (Noah's Ark Lab, Huawei) Wei Fan (Huawei Noah’s Ark Lab) Cheng He (Noah's Ark Lab, Huawei) Jianfeng Zhang (Noah's Ark Lab, Huawei) Jianfeng Qian (Huawei Noah's Ark Lab) Geoffrey Holmes (University of Waikato) Bernhard Pfahringer (University of Waikato) Nowadays real-time industrial applications are generating a huge amount of data continuously every day. To process these large data streams, we need fast and efficient methodologies and systems. A useful feature desired for data scientists and analysts is to have easy to visualize and understand machine learning models. Decision trees are preferred in many real-time applications for this reason, and also, because combined in an ensemble, they are one of the most powerful methods in machine learning. In this paper, we present a new system called streamDM-C++, that implements decision trees for data streams in C++, and that has been used extensively at Huawei. Streaming decision trees adapt to changes on streams, a huge advantage since standard decision trees are built using a snapshot of data, and can not evolve over time. streamDM-C++ is easy to extend, and contains more powerful ensemble methods, and a more efficient and easy to use adaptive decision tree. We compare our new implementation with VFML, the current state of the art implementation in C, and show how our new system outperforms VFML in speed using less resources. More on http://www.kdd.org/kdd2017/
Views: 409 KDD2017 video
Lingo4G large-scale text clustering engine, workflow overview
 
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Carrot Search Lingo4G is a next-generation text clustering engine capable of processing tens of gigabytes of text and millions of documents. This video is a more in-depth overview of Lingo4G workflow. We index and analyze 240k questions and answers posted to the computer enthusiasts Q&A site, superuser.com. Lingo4G documentation: http://get.carrotsearch.com/lingo4g/l... Lingo4G trial and more information: https://carrotsearch.com/lingo4g
Views: 422 Carrot Search
MongoDB: A Modern Database for Modern Healthcare Applications
 
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MongoDB: A Modern Database for Modern Healthcare Applications Presented by: Matt Bates, MongoDB This presentation filmed at the Open Source Skunkworks stand at EHI Live 2014 on Tuesday 5th November 2014. For more information about Open Source Skunkworks, visit: http://guildfoss.com For more information about EHI Live, visit: http://www.ehilive.co.uk Video production by Event Amplifier http://eventamplifier.com
Trendrating Introduction
 
06:56
Trendrating is an innovative, well-tested rating system designed to assess the direction and the quality of medium-term price trends for STOCKS, ETFS, INDEXES, CURRENCIES. The rating system is based on 4 grades: A= strong bull trend B= bull trend C= bear trend D= strong bear trend Trendrating filters out price swings and false moves and captures the true underlying trends. It keeps the noise out and analyzes the real medium term direction of price. Trendrating is based on a self-adaptive model using multivariate data analysis on large populations of historical price patterns. The pattern recognition is performed through numerical analysis of historical prices, volumes and volatility. The model is optimized on the basis of statistical evidence of recurring patterns that lead to relevant trends spanning from a few months to a few quarters. The accuracy of Trendrating has been massively tested on 20,000 time series across 25 years of daily data. Results are impressive and fully transparent to our customers. Professional managers are able to measure almost everything: fundamental ratios, estimates, volatility, correlation, risk. But they are missing tools to measure one of the most important factors – the price trend. Trendrating is changing this. Trendrating makes it possible to fill this critical information gap by offering effective and easy to use metrics to measure and compare medium term trends for 20,000 instruments.
Views: 119 Trendrating
Elasticsearch - Introduction
 
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Learn more: https://www.elastic.co/webinars/getting-started-elasticsearch?blade=video&hulk=youtube Time flies since Shay created this video about what Elasticsearch is and how to use it.
Views: 77114 Elastic
EFFECTIVE STORAGE AND RETRIEVAL OF BIG DATA AND ULTIMATE FILTER FROM ATTACKS(UFFA)
 
18:44
There are many data parallel computing frameworks such as MapReduce and Hadoop to run data intensive applications but they exhibit interest locality which only sweep part of a big data set. In this paper, the enhancing of this data grouping with Bigdata distributed database is done which uses Index-Based Clustering of database and indexes all the clusters from different locations. It achieves anonymity with database by avoiding individual details towards traversing data. It sends only the necessary data rather than sending their personal details. It uses Admin system which has research pattern rights thus helps in authentication and trust for data before updating to the main database. Local system should have encrypted data so that nobody can get any individuals database. It uses database filter Ultimate Filter From Attacks (UFFA) for avoiding database attacks.
Prof. Lucie Guibault: "Intellectual property rights' obstructions to text and data mining"
 
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In the last few years, collections of digital text have strongly increased in number, especially in the field of humanities. Digital libraries of full-text documents, including digital editions of literary texts, are emerging as environments for the production, the management and the dissemination of complex annotated corpora. The potential of Text and Data Mining (TDM) technology is enormous. If encouraged, TDM can become an everyday tool used for the discovery of knowledge, to create significant benefits for industry, citizens and governments. Because TDM involves certain acts of reproduction and communication to the public of (parts of) the texts in the collections, the enforcement of copyright and database rights in the collections may constitute a serious obstacle to the use of this new technology for the benefit of science. The intellectual property implications of the use of TDM has been brought to the fore at the European level, where TDM was declared one of the four topics needing further discussion in the context of the structured stakeholder dialogue led by the European Commission.The presentation will explain how copyright and database rights can be used to restrict TDM and how discussions are evolving on this issue at the European level. The interdisciplinary lecture series "Internet & Society", organised by the Institute of Political Science and the Sociological Research Institute, as part of the Digital Humanities Research Collaboration, explores the social, technological and political interactions of the Internet and society. More information can be found under http://www.gcdh.de/index.php?cID=341.