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Here are some general guidelines to make your use of this site more enjoyable and productive for you At EE, the experts exchange answers and advice for points. If you look at the questions awaiting answers in this zone, you will see a lot of point questions. Your question is competing for the experts’ attention among those high-point questions. So as a matter of simple economics you might be able to envision which questions will get the experts’ attention first. We are experts, but not mind readers.

Matchmaking Portal for the Discovery of Numerical and Symbolic Services

We’ve run into a number of situations where we need to normalize company names in a database for consistency. It’s usually not impossible to precisely sanitize and validate company names on the input side, unless you have a solid dataset of company names like Facebook or LinkedIn that you can validate against. This leads to vast inconsistencies with company name values in most databases and datasets. Data Normalization To solve this problem we need a good normalization process.

Unfortunately normalizing company names accurately is a difficult task to do well because of the free form nature of a company name.

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Tommi has been running Blueprint Genetics since the beginning and is responsible for sales management, business development, finance and administration. Tommi holds a M. Tech from the Helsinki University of Technology where he studied business strategy and applied mathematics. The Bellabeat team is behind the Leaf health tracking jewelry for women – one of the best mobile products of according to Digital Trends; Spring, the first smart water bottle powered by AI and Shell, the innovative non-invasive fetal heart monitor.

In Urska was chosen by Forbes as ambassador of the Technology field for their first European 30 under 30 list. She was nominated as best female entrepreneur in Croatia in by Veuve Clicquot and has also received the Jabolko Navdiha Apple of Inspiration award from the President of the Republic of Slovenia. Bellabeat was part of the Winter14 batch at Y Combinator where it completed the program top of its class. Indu Subaiya Co-founder of Health 2. Indu co-founded and served as CEO of Health 2.

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Database used is MySQL database see attachment. Background We have created web crawlers to crawl various sites for data. This data is being populated into various tables in the database. Keyword Matching Script — Script to ensure that duplicate records are not being created for the same information. Code must gauge the proximity of the match, based on an algorithm which matches key words.

Matchmaking Portal for the Discovery of Numerical and Symbolic Services Simone A. Ludwig1, Omer F. Rana1, William Naylor2 and Julian Padget2 1 School of Computer Science/Welsh eScience Centre, Cardiff University 2 Department of Computer Science, University of Bath Abstract A significant number of applications within eScience make use of numerical algorithms, developed as part of a .

Three Ways to Search Smarter in The bigger and more complicated the database, the more talented its search capabilities need to be. These three techniques let you broaden the results returned by MySQL searches: A database doesn’t do users much good if they can’t find the data they’re searching for. The larger and more complex the database elements, the more sophisticated your searches have to be to return the information you need. The three types of full-text searches are natural language, boolean, and query expansion, which broadens the results of a natural-language search based on automatic relevance feedback also called blind query expansion.

The tutorial uses the example of a search of a car database with and without query expansion. A MySQL full-text search without query expansion top and with query expansion bottom. The simplest is a SQLDump and then search the database as a file. Another option is to use the search feature in phpMyAdmin: A third solution is the following function: Stack Overflow Use SoundEx to include approximations in search results Approximate string matching is an algorithmic trick upon which empires have been built.

However, the fuzzy searching techniques that are common in Web search engines are lacking in MySQL, at least in terms of a direct equivalent search function.

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The algorithm requires me to be able to take ownership of records in a database in a fast, first-come-first-serve, way which locks these records out of subsequent requests until they are released. If the swap succeeds the function returns true, if not it returns false. All of this is done atomically and on most modern hardware and JVMs this is implemented as a single instruction.

The idea behind this function is that a thread which wants to obtain or change a resource tries to do so with minimal overhead and no global locking required. If the swap fails the thread is responsible for either trying again or giving up; the key here is that the caller the thread is the only part which might block or wait, all other threads accessing the resource can continue uninterrupted. Contrast this to a global lock or synchronisation approach where the thread effectively locks out everybody else when accessing the resource.

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Syslog only works on Linux. For MongoDB, user, password, and port data are optional, and can be separated by commas to use multiple hosts in order to use a replica set. Cycle in seconds to rotate activity logs. Cycle in seconds to flush Google logging. Period in days to keep old glog files. Stale files older than this period will be erased. JSON file containing maintenance data.

Interval in seconds to refresh system info. For more details, please refer to Distributed processing part 2: Data sharing between servers. Whether or not to enable this feature. Redis server tag value to use as storage to share data between servers. This feature controls connections to Redis.

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Engineering String Matching using Scoring Matrices repost I need to have a program that looks for repeated matches of sequence y in sequence x. This would employ the “Repeated Matches” algorithm that is used in Biological sequence analysis. The program would have to be written in Perl, and not complex, and with good comments.

Currently, I use a genetic algorithm to solve the three cases. PvE: my genome is my lineup, each chromosome being a card. My fitness is basically the result of the battle simulation, where I try to find the first solution that works. Multiplayer matchmaking algorithm. 4. sh script for executing mysql script with entering pass.

Edit Distance and the Levenshtein algorithm Sometimes you need to know how similar words are, rather than whether they are identical. To get a general measure of similarity is tricky, impossible probably, because similarity is so strongly determined by culture. To gauge this sort of similarity, you need to know the context of language, pronunciation, culture and semantics. However, we can fall back on more scientific measures based on comparing sequences, though they will never be perfect.

How many edits are needed to convert the first string into the second? Edits can be insertions, deletions, replacements or transpositions. The Dynamic approach to finding an edit distance is best done with a matrix. Solutions for this exist for all manner of languages. SQL is handicapped by having no support for matrices.

An equivalent relation can be used instead, but it is overkill, and slow. The fastest dynamic solutions in SQL tend to use Unicode strings as cod matrices since these can be cajoled into use as integer matrixes, but they are painful to use and debug. Levenshtein Distance, developed by Vladimir Levenshtein in , is the algorithm we learn in college for measuring edit-difference.

The Damerau-Levenshtein algorithm for Edit Distance solves this.

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And most database servers also implement their own SQL commands to search for fuzzy duplicates, dupes with words that sound similar. This simple algorithm sometimes delivers reasonably good results. Thus, for example, ‘Smith’ and ‘Smythe’ are recognised to be identical.

Michael is a top architect, engineer, developer, and entrepreneur with a proven ability to develop efficient, scalable, and fault-tolerant server solutions for complex problems. He has extensive experience and skills with all levels of software and architecture.

The score will be based on the importance of the match: We will filter those out and feel free to add any word you think is irrelevant. Another thing is, we want to limit the length of our query. We don’t want a user to write a novel in the search field and crash our MySQL server. The way we will implement our algorithm is by giving a score every time we find a match.

We will match words using the if statement and accumulate points as we match more words.

Evans Yeung

It is important to attempt to identify neighboring businesses as well as common areas. Owner Once the physical locations have been identified, it is useful to identify the actual property owner s. This can either be an individual, group, or corporation. If the target corporation does not own the property then they may be limited in what they can physically do to enhance or improve the physical location.

The information recorded and level of transparency varies greatly by jurisdiction.

JDK ,JSP,HTML,MYSQL 39 A Distributed Algorithm for Finding All Best Swap Edges of a Minimum Diameter Spanning Tree (IEEE) Jdk, java swing 72 Service Operator-aware Trust Scheme for Resource Matchmaking across Multiple Clouds JDK ,JSP,HTML,MYSQL.

I am trying to write a solver for a sort of card game. I mainly do that for fun, and also to be able to learn a bit about the different types of algorithms I could use for this problem. The rules of the card game is pretty simple: A card has a given amount of HP and Attack and potentially a skill that increases its stats Each card has an element.

Each element has a weakness. An enemy can attack you. The engine selects one random lineup for each player and the result of this battle determines the winner of the attack Tournaments: All players battle against each other. Each lineup will confront its opposite lineup i. The winner is selected based on the amount of wins. I have made a battle simulator that gives you the amount of damage made by the winner positive amount if left side wins, negative otherwise.

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Get it at your local newsstand, or better yet, subscribe at The Windows Change Journal is a database that contains a list of every change made to the files or directories on an NTFS 5. Each volume has its own Change Journal database that contains records reflecting the changes occurring to that volume s files and directories. Jeffrey Cooperstein is an author, trainer, and Windows programming consultant.

Jeff is a consultant and teaches Win32 programming courses. He can be reached at Windows is packed with new and exciting technologies, and the Change Journal is one of them. The Change Journal is going to open up a whole new world of features in future Windows-based applications, and it will provide the opportunity for dramatic performance improvements in many of today s applications.

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GroupName varchar The Profile table stores profile data for a user. In that table are group columns. Each group is given a name for admin console purposes. In my case a group is related stats about a user such as “Physical Makeup”. This group could contain eye color, hair color, body type, etc. All of these makeups are stored in the Group1 column in a base2 format. So, blue eyes is 1, brown eyes is 2, green eyes is 4, hazel eyes is 8, brown hair is 16, blonde hair is 32 you get the idea here – leave space between sub group features for additions.

Then you can do a bitwise search on this column

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SHARE Although studying creativity is considered a legitimate scientific discipline nowadays, it is still a very young one. In the early s, a psychologist named J. Guilford was one of the first academic researchers who dared to conduct a study of creativity. He challenged research subjects to connect all nine dots using just four straight lines without lifting their pencils from the page.

Today many people are familiar with this puzzle and its solution.

How Not To Sort By Average Rating. By Evan Miller. February 6, (). Translations: German Russian Ukrainian Estonian PROBLEM: You are a web have users. Your users rate stuff on your site. You want to put the highest-rated stuff at the top and lowest-rated at the bottom.

SQL Server Analysis Services Azure Analysis Services An algorithm in data mining or machine learning is a set of heuristics and calculations that creates a model from data. To create a model, the algorithm first analyzes the data you provide, looking for specific types of patterns or trends. The algorithm uses the results of this analysis over many iterations to find the optimal parameters for creating the mining model.

These parameters are then applied across the entire data set to extract actionable patterns and detailed statistics. The mining model that an algorithm creates from your data can take various forms, including: A set of clusters that describe how the cases in a dataset are related. A decision tree that predicts an outcome, and describes how different criteria affect that outcome.

A mathematical model that forecasts sales. A set of rules that describe how products are grouped together in a transaction, and the probabilities that products are purchased together. The algorithms provided in SQL Server Data Mining are the most popular, well-researched methods of deriving patterns from data. To take one example, K-means clustering is one of the oldest clustering algorithms and is available widely in many different tools and with many different implementations and options.

All of the Microsoft data mining algorithms can be extensively customized and are fully programmable, using the provided APIs.

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