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Search results “Indexing and mining large time series databases”
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: 341 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: 4391 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: 4632 Google
Indexing for Time Series
 
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Recorded with http://screencast-o-matic.com
Views: 129 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: 1480 GoogleTechTalks
How to Build a Highly Available Time Series Database in KairosDB
 
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A highly available time-series solution requires an efficient tailored front-end framework and a backend database with a fast ingestion rate. KairosDB provides a simple and reliable way to ingest and retrieve sensors’ information or metrics, while Scylla provides a highly reliable and performant backend database that scales indefinitely, and can store large quantities of time-series data. Hear ScyllaDB solution architect Eyal Gutkind and Proofpoint engineer Brian Hawkins present an informative webinar on... - Steps for building an efficient TSDB solution with Scylla and KairosDB - Real-world use cases and metrics - Considerations when choosing time series solutions
Views: 374 ScyllaDB
InfluxDB Storage Engine Internals | DataEngConf SF '17
 
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Don’t miss the next DataEngConf in Barcelona: https://dataeng.co/2O0ZUq7 Recorded at DataEngConf SF '17 nfluxDB is an open source time series database developed over the last 3 years. In that time we've tried different storage engines starting with LevelDB and testing out HyperLevelDB, RocksDB and BoltDB. Over a year ago we made the decision to write our own storage engine from scratch. Inspired by the LSM Tree underlying LevelDB and its variants, we created a new storage engine we're calling the TSM Tree (Time Structured Merge Tree). Over the last eight months we've added to this storage engine to provide index capabilities for mapping metadata to underlying time series. This talk will briefly cover our journey with other storage engines and why we ultimately decided to write our own from scratch. The underlying InfluxDB storage engine is more like two storage engines in one: a time series storage engine and an inverted index for metadata. This talk will dive into the details about how each of these systems work, their design considerations and lessons learned along the way. We'll cover compression techniques for columnar time series storage, Robin Hood Hashing for quickly index lookups, and sketches for estimation of series cardinality at scale. Speaker: Paul Dix, Metamarkets
Views: 2100 Hakka Labs
Ted Dunning & Ellen Friedman on "Time Series Databases" - Strata Europe 2014
 
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From Strata + Hadoop World 2014 in Barcelona, MapR's Ted Dunning and author Ellen Friedman discuss their O'Reilly publication "Time Series Databases". Find the book here: http://shop.oreilly.com/product/0636920035435.do Watch more from Strata Europe 2014: http://goo.gl/uqw6WS Visit the Strata website to learn more: http://strataconf.com/strataeu2014/ About Ellen Friedman (Consultant for Strategic Content:) Ellen Friedman is a solutions consultant, scientist and author, currently writing about a variety of open source and big data topics including being co-author of Mahout in Action (Manning), the Practical Machine Learning series from O’Reilly, and the newest title, Time Series Databases (O’Reilly). She is a committer on the Apache Mahout project, a contributor to Apache Drill and has been an invited speaker at Berlin Buzzwords 2013, the Philly ETE 2014 conference and keynote speaker for NoSQL Matters 2014 in Barcelona. With a Ph.D. in biochemistry and years of work writing on a variety of scientific and computing topics, she is an experienced communicator. She’s also co-author of a book of magic-themed cartoons, A Rabbit Under the Hat. Follow on Twitter as @Ellen_Friedman. About Ted Dunning (Chief Application Architect, MapR): Serial startup and artist and open-source innovator, particularly interested in large data systems and statistical modeling. Stay Connected to O'Reilly Media by Email - http://goo.gl/YZSWbO Follow O'Reilly Media: http://plus.google.com/+oreillymedia https://www.facebook.com/OReilly https://twitter.com/OReillyMedia
Views: 587 O'Reilly
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: 529 Microsoft Research
Stanford Seminar - The Case for Learned Index Structures
 
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EE380: Computer Systems Colloquium Seminar The Case for Learned Index Structures Speaker: Alex Beutel and Ed Chi, Google Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this talk, we take this premise and explain how existing database index structures can be replaced with other types of models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of indexed data and use this signal to effectively predict the position or existence of records. We offer theoretical analysis under which conditions learned indexes outperform traditional index structures and we will delve into the challenges in designing learned index structures. Through addressing these challenges, our initial results show that learned indexes are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. Finally, we will discuss the broader implications of learned indexes on database design and future directions for the ML for Database Systems research. About the Speaker: Alex Beutel is a Senior Research Scientist in the Google Brain team working on neural recommendation, fairness in machine learning, and ML for Systems. He received his Ph.D. in 2016 from Carnegie Mellon University's Computer Science Department, and previously received his B.S. from Duke University in computer science and physics. His Ph.D. thesis on large-scale user behavior modeling, covering recommender systems, fraud detection, and scalable machine learning, was given the SIGKDD 2017 Doctoral Dissertation Award Runner-Up. He received the Best Paper Award at KDD 2016 and ACM GIS 2010, was a finalist for best paper in KDD 2014 and ASONAM 2012, and was awarded the Facebook Fellowship in 2013 and the NSF Graduate Research Fellowship in 2011. More details can be found at http://alexbeutel.com. Ed H. Chi is a Principal Scientist at Google, leading machine learning research focusing on neural modeling and recommendation systems in the Google Brain team. He has launched significant improvements for YouTube, Google Play Store and Google+. With 39 patents and over 110 research articles, he is known for research on user behavior in web and social media. Prior to Google, he was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group, where he led the team in understanding how social systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. Recognized as an ACM Distinguished Scientist and elected into the CHI Academy, he has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. Recognized recently with a 20-year Test of Time award for research in information visualization, Ed is also an avid swimmer, photographer and snowboarder in his spare time, and has a blackbelt in Taekwondo. For more information about this seminar and its speaker, you can visit https://ee380.stanford.edu/Abstracts/181017.html Support for the Stanford Colloquium on Computer Systems Seminar Series provided by the Stanford Computer Forum. Colloquium on Computer Systems Seminar Series (EE380) presents the current research in design, implementation, analysis, and use of computer systems. Topics range from integrated circuits to operating systems and programming languages. It is free and open to the public, with new lectures each week. Learn more: http://bit.ly/WinYX5
Views: 210 stanfordonline
Comparing Time 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: 4179 Wild About Statistics
Data Analytics with MongoDB
 
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Presented by MongoDB's Grigori Melnik and Michael Gordon at MongoDB World 2018. Data analytics can offer insights into your business and help take it to the next level. In this talk you'll learn about MongoDB tools for building visualizations, dashboards and interacting with your data. We'll start with exploratory data analysis using MongoDB Compass. Then, in a matter of minutes, we'll take you from 0 to 1 - connecting to your Atlas cluster via BI Connector and running analytical queries against it in Microsoft Excel. We'll also showcase the new MongoDB Charts product and you'll see how quick, easy and intuitive analytics can be on the MongoDB platform without flattening the data or spending time and effort on complicated and fragile ETL.
Views: 164 MongoDB
4. Vector Indexing and Slicing in R
 
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Vector indexing and slicing allows you to return certain indexed or sliced data from your vectors. Perhaps best to watch video 3, before progressing to this.
Views: 114 Gary Hutson
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: 72 IRT SystemX
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: 700 Heavybit
Druid: A Real-Time Analytical Data Store
 
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Presented by Fangjin Yang (Software Engineer) at Berkeley's AMPLab.
Views: 2316 Metamarkets
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: 1775 Databricks
FCMS - SSP2015  Investigating Time Series Database
 
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Summer Scholarship Project 2015 by Andy Bell University of Waikato – Faculty of Computing and Mathematical Sciences
Views: 36 UOW FCMS-SSP2015
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: 1857 Andrea Ross
Oracle SQL Tuning - Data Warehouse Star Transformations, Even with Standard Edition!
 
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Joining large tables in a Data Warehouse will often generate star transformations in the execution plan. So, what is a star transformation? And, what if you don't have Enterprise Edition - so you can't build Bitmap Indexes? In this free tutorial, Oracle Master John Watson will demonstrate star transformations in EE and how to make them work even without bitmap indexes - Standard Edition can save you thousands in licensing fees! This is Part 3 of a 5 Part series. View all video tutorials at SkillBuilders.com/EqualSQL.
Views: 4525 SkillBuilders
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 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: 72458 Steve Fox
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.
Data Mining for prediction of Human Development Index
 
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Carnegie Mellon University Heinz College
Views: 992 Lars Reeker
Data Mining in SQL Server Analysis Services
 
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Presenter: Brian Knight
Views: 96985 PASStv
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
How to Efficiently Train Your Orca Mining Skills! - EVE Online
 
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Learn how to train your Orca Mining Skills efficiently and skip all of the headache of researching it yourself! If you want the text format of the skill queue, you can check it out here. http://news.markeedragon.com/how-to-efficiently-train-your-orca-mining-skills/ http://store.markeedragon.com/affiliate.php?id=4&redirect=index.php?cat=4 Special Viewer Discount or Bonus. YOUR CHOICE! Want a bonus on your EVE new account or Plex? Use the discount code of "discount" and get 3% off your order. Or want 3.3% cash back for even more savings? Use bonus code "bonus" and get 3.3% credit in your account for future purchases This is for a limited time and the discount/bonus codes may be changed or removed at any time. The discount / bonus is provided by Markee Dragon Game Codes and we are an authorized CCP reseller. Codes delivered in 20 minutes or less. Twitter: http://twitter.com/markeedragon What's my computer build? Want to know what other stuff I use? Find it here: https://www.amazon.com/shop/markeedragon Want to Try EVE for free? Get it here: http://secure.eveonline.com/signup/?invc=d6baec26-231d-4ced-9cd2-1a8b3713d72d&action=buddy Join us for chat in Discord https://discord.gg/markeedragon Discord is what we use for in game chat and voice comms. This is a simulcast of http://twitch.tv/markeedragon . You can watch here live on YouTube and talk in chat. but for the giveaways mentioned on the show those currently only work in Twitch chat. You do not have to watch on Twitch. You only need to be in the Twitch chat to get in on the giveaways. WTFast is what I use to improve my connection to EVE. I get at least a 20% improvement at all times. Try it here: http://www.wtfast.com/markeedragon Videos How to convert Loyalty Points This video shows how we decided what items to use. Items Sold. https://www.youtube.com/watch?v=rnv4eW9hwP0 What worked Successful conversion of 1m LP to 1.5b ISK in 13 days. https://www.youtube.com/watch?v=eiGEj4XhKt0 Hauling Introduction https://www.youtube.com/watch?v=7NjY9aU-uBQ Hauling is a great secondary income. Market Blue Line Hauling is a great secondary income. EVE Sites Mentioned on the Show EVE Guides and Ship Fits: http://news.markeedragon.com/category/game-guides-how-to/eve-online-guides/ Moose Army Corp - My Null Corp I am a member of. http://moose.army Airhogs - The best all around corp and great for new players. I am also a member http://docs.google.com/document/d/1cCPjgTfOxMN7JpagSs5iUCyES2OalT5j6O4zaP5qVNw/edit?usp=sharing LP Store Conversion See what LP items are currently worth on the market. https://www.fuzzwork.co.uk/lpstore/ Daopa's LP Stores Database LP store items information http://www.ellatha.com/eve/LP-Stores EVE Assets Manager Find your stuff. Know where your money is sitting! http://eve.nikr.net/jeveasset EVE Markets Market history data. http://eve-markets.net/ EVEPraisal Quick values for your loot and other market actions. http://evepraisal.com/ EVE Maps All kinds of map related information. http://evemaps.dotlan.net/ Deepsafe https://deepsafe.xyz Excellent crowed sourced information on cosmic signatures. All explorers should use this. EVE University EVE Wiki http://wiki.eveuniversity.org/Main_Page Music by Monstercat http://www.monstercat.com
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: 390 The Audiopedia
What is SPATIOTEMPORAL DATABASE? What does SPATIOTEMPORAL DATABASE mean?
 
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What is SPATIOTEMPORAL DATABASE? What does SPATIOTEMPORAL DATABASE mean? SPATIOTEMPORAL DATABASE meaning - SPATIOTEMPORAL DATABASE definition - SPATIOTEMPORAL DATABASE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. A spatiotemporal database is a database that manages both space and time information. Common examples include: Tracking of moving objects, which typically can occupy only a single position at a given time. A database of wireless communication networks, which may exist only for a short timespan within a geographic region. An index of species in a given geographic region, where over time additional species may be introduced or existing species migrate or die out. Historical tracking of plate tectonic activity. At first glance, spatiotemporal databases are an extension of spatial databases. A spatiotemporal database embodies spatial, temporal, and spatiotemporal database concepts, and captures spatial and temporal aspects of data and deals with geometry changing over time and/or location of objects moving over invariant geometry (known variously as moving objects databases or real-time locating systems) However, although there exist numerous relational databases with spatial extensions, the spatiotemporal databases are not based on the relational model for practical reasons, chiefly among them that the data is multi-dimensional and capturing complex structures and behaviours. As of 2008, there are no RDBMS products with spatiotemporal extensions. There are some products such as the open-source TerraLib which use a middleware approach storing their data in a relational database. Unlike in the pure spatial domain, there are however no official or de facto standards for spatio-temporal data models and their querying. In general, the theory of this area is also less well-developed. Another approach is the constraint database system such as MLPQ (Management of Linear Programming Queries).
Views: 957 The Audiopedia
Data manipulation with R tutorial
 
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Learn how to use R to manipulate data in this easy to follow, step-by-step guide. Includes getting set up with R, loading data, data frames, asking questions of the data, basic dplyr; including the 5 verb commands, select, filter, arrange, mutate, and summarize. Find out how to write code in a clear and logical way with functions including piping. Visit https://deltadna.com/blog/data-manipulation-with-r/ for downloadable data to work with during this language tutorial. Installing R 01:00 Direct Access 03:50 Data-frames 07:39 Vectors 09:30 Basic dplyr 0:11:28 Answering a question, using dplyr 0:16:33 Using pipes 0:19:44 Answering another question, using dplyr and pipes 0:22:48 Answering a more difficult question 0:26:38
Views: 15848 deltaDNA
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: 737 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: 23500 CITRIS
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.
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
Type Less, Find More: Fast Autocompletion Search with a...
 
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Google TechTalks August 14, 2006 Holger Bast ABSTRACT We consider the following full-text search autocompletion feature. Imagine a user of a search engine typing a query. Then with every letter being typed, we would like an instant display of completions of the last query word which would lead to good hits. At the same time, the best hits for any of these completions should be displayed. Known indexing data structures that apply to this problem either incur large processing times for a substantial class of queries, or they use a lot of space. We present a new indexing data structure that uses no more space than a state-of-the-art compressed inverted index, but that yields an order of magnitude...
Views: 1525 Google
How to Import Data, Copy Data from Excel to R: .csv & .txt Formats (R Tutorial 1.5)
 
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Import/copy data from excel (or other spreadsheets) into #R using both comma-separated values and tab-delimited text file. Find more #RStats and #Statistics Tutorials here: https://goo.gl/4vDQzT ▶︎ 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 #RStatisticalSoftware and #RStudio 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 *****************************************************************************************To learn more: Subscribe: https://goo.gl/4vDQzT website: http://statslectures.com Facebook:https://goo.gl/qYQavS Twitter:https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at #UBC. Producer: Ladan Hamadani (B.Sc., BA., MPH)
Scaling Machine Learning on Industrial Time Series with Cloud Bigtable and AutoML (Cloud Next '18)
 
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The saying goes that machine learning is about data and algorithms, but mostly data. In a real-world industrial setting, this data is usually messy, error-laden, and inconsistent. This session will present how Cognite is using a wide range of tools in Google Cloud Platform, including Cloud Bigtable, Cloud PubSub, Cloud SQL and AutoML, to address the key pain points in a scalable machine learning workflow: - Live data preparation and aggregation. - Data contextualization at scale. - Implementation and operationalization of models. IO223 Event schedule → http://g.co/next18 Watch more Infrastructure & Operations sessions here → http://bit.ly/2uEykpQ Next ‘18 All Sessions playlist → http://bit.ly/Allsessions Subscribe to the Google Cloud channel! → http://bit.ly/NextSub
Views: 1662 Google Cloud Platform
Lessons learned from managing way too many database servers
 
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Rob Wultsch, a recent addition to Facebook's DBA Operations team, will compare and contrast the Facebook deployment with his previous work with another large deployment of open source databases. In this db-agnostic talk, he'll go over: the bizarre becoming ordinary, the many types of infrastructure that do not scale and what does and does not work when building a DBA team.
Views: 1014 SF MySQL Meetup
B2B Big Data Challenges, Nick Mehta, Gainsight (Data Driven NYC / FirstMark Capital)
 
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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: 434 Data Driven NYC
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: 334621 APMonitor.com
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: 433 Carrot Search
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: 17704 GIS VideosTV
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
BOB'S BIG ROCKET - Bob's Mods Factorio - Part 139
 
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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: 1770 Steejo
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://bit.ly/JoinCCNewsletter 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 - http://bit.ly/ccbluehost (The best web hosting for a cheap price!)
Views: 15915 Caleb Curry
Mastering the Lucene Index: Post-processing, Performance, Results
 
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Lucene is at the heart of open source search: the index is at the heart of Lucene. Lucene/Solr index implementation is critical to the performance of your search application and the quality of your results — and not just at indexing time. If you're developing applications in Lucene/Solr, your index will reward care and attention — adding power to your running search application — all the more so as you inevitably increase the scope of your query traffic and the dimensions of your data.
Views: 2662 LucidImagination
LAPD Data Mining Project
 
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Senior Data Mining Project WTAMU
Views: 90 James Ritter
011. Discovering Common Motifs in Mouse Cursor Movement Data - Дмитрий Лагун
 
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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.
FAME database
 
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Information and Library Services Manager Andy Priestner describes the FAME database.
Views: 534 CJBSInfoLib
Moon Mining TOTAL MINED ISK 9,038,335,612.95 - Giveaways - EVE Online Live
 
02:31:49
Want an entry on the Live Show Giveaway while the show is live? Get your entry here. https://store.markeedragon.com/index.php?cat=18 We will draw near the end of the live show and the winner will be notified by email. If you are viewing the giveaways while the show is offline only monthly giveaways will appear. http://store.markeedragon.com/affiliate.php?id=4&redirect=index.php?cat=4 Special Viewer Discount or Bonus. YOUR CHOICE! Want a bonus on your EVE new account or Plex? Use the discount code of "discount" and get 3% off your order. Or want 3.3% cash back for even more savings? Use bonus code "bonus" and get 3.3% credit in your account for future purchases This is for a limited time and the discount/bonus codes may be changed or removed at any time. The discount / bonus is provided by Markee Dragon Game Codes and we are an authorized CCP reseller. Codes delivered in 20 minutes or less. Twitter: http://twitter.com/markeedragon What's my computer build? Want to know what other stuff I use? Find it here: https://www.amazon.com/shop/markeedragon Want to Try EVE for free? Get it here: http://secure.eveonline.com/signup/?invc=d6baec26-231d-4ced-9cd2-1a8b3713d72d&action=buddy [!nojob] Want to get rid of your day job and be your own boss? Markee Dragon is sharing how he does it with a step by step guide to independence. Get it here: http://jedimarketingtricks.com/ultimate/ Join me and your favorite streamers! Put my 10+ years of experience to work for YOU. Learn how to start generating income while playing your favorite games! I include everything you need to know to get started PLUS plenty of support for when you get stuck! Join us for chat in Discord https://discord.gg/markeedragon Discord is what we use for in game chat and voice comms. This is a simulcast of http://twitch.tv/markeedragon . You can watch here live on YouTube and talk in chat. but for the giveaways mentioned on the show those currently only work in Twitch chat. You do not have to watch on Twitch. You only need to be in the Twitch chat to get in on the giveaways. WTFast is what I use to improve my connection to EVE. I get at least a 20% improvement at all times. Try it here: http://www.wtfast.com/markeedragon Videos How to convert Loyalty Points This video shows how we decided what items to use. Items Sold. https://www.youtube.com/watch?v=rnv4eW9hwP0 What worked Successful conversion of 1m LP to 1.5b ISK in 13 days. https://www.youtube.com/watch?v=eiGEj4XhKt0 Hauling Introduction https://www.youtube.com/watch?v=7NjY9aU-uBQ Hauling is a great secondary income. Market Blue Line Hauling is a great secondary income. EVE Sites Mentioned on the Show EVE Guides and Ship Fits: http://news.markeedragon.com/category/game-guides-how-to/eve-online-guides/ Moose Army Corp - My Null Corp I am a member of. http://moose.army Airhogs - The best all around corp and great for new players. I am also a member http://docs.google.com/document/d/1cCPjgTfOxMN7JpagSs5iUCyES2OalT5j6O4zaP5qVNw/edit?usp=sharing LP Store Conversion See what LP items are currently worth on the market. https://www.fuzzwork.co.uk/lpstore/ Daopa's LP Stores Database LP store items information http://www.ellatha.com/eve/LP-Stores EVE Assets Manager Find your stuff. Know where your money is sitting! http://eve.nikr.net/jeveasset EVE Markets Market history data. http://eve-markets.net/ EVEPraisal Quick values for your loot and other market actions. http://evepraisal.com/ EVE Maps All kinds of map related information. http://evemaps.dotlan.net/ Deepsafe https://deepsafe.xyz Excellent crowed sourced information on cosmic signatures. All explorers should use this. EVE University EVE Wiki http://wiki.eveuniversity.org/Main_Page Live shows Schedule: https://docs.google.com/spreadsheets/d/1zQZoKQnzGRgWXBefzGKlyfDppGQWzJy6PafWm-4c4sQ/pubhtml Music by Monstercat http://www.monstercat.com