I want to use Apache Mahout as Recommendation Engine; but over here I found that it force us to use its own table called taste_preferences with only 3-4 columns and data type as number(Long/big int). Is it mandatory to use this table and store data in number format only.
That is one way to build a recommendation engine, but there are simpler ways as well.
There is a small book available for free from
http://www.mapr.com/practical-machine-learning
which explains a way to deploy recommendation engines on top of a search engine. This requires an off-line analysis to build the data that gets put into the search engine, but once you have the indicator data in the search engine, you can do recommendations using search queries. These queries are not textual queries, but instead use past behavior as a query.
You can also see slides describing the approach here:
http://www.slideshare.net/tdunning/building-multimodal-recommendation-engines-using-search-engines
and here:
http://www.slideshare.net/tdunning/using-mahout-and-a-search-engine-for-recommendation
The book is easier to understand than the slides without the narrative, but both are likely useful since the slides have more details.
Related
I have a table with Id and Text fields. The Text field holds sentences, averaging 50 words. There are >1,000,000 rows.
This is part of a web app where users need to be able to search through these sentences. Here's the twist though - I need to be able to run a custom search function written in C# that uses Machine Learning instead.
From what I understand, this means I'll have to download the entire database of >1,000,000 rows every time a user makes a search! This seems really inefficient to me.
How would you implement this in the most efficient/fast way possible?
If this is relevant, I'm using EF Core with LINQ .Where(my_custom_search_function), with a PostgreSQL database
I think I've found the solution. Postgresql full-text search currently provides two ranking functions. In this case "sorting" in the question and "ranking" here refer to the same thing.
Postgresql docs state:
However, the concept of relevancy is vague and very application-specific. Different applications might require additional information for ranking, e.g., document modification time. The built-in ranking functions are only examples. You can write your own ranking functions and/or combine their results with additional factors to fit your specific needs.
These functions can any of the four kinds of supported postgresql functions.
Then they answer this exact question:
Ranking can be expensive since it requires consulting the tsvector of each matching document, which can be I/O bound and therefore slow. Unfortunately, it is almost impossible to avoid since practical queries often result in large numbers of matches.
Credits to #Used_By_Already for pointing me to Postgresql full-text search.
I am currently working on a private project that is going to use Google's GTFS spec to get information about 100s of Public Transit agencies, their routers, stations, times, and other related information. I will be getting my information from here and the google code wiki page with similar info. There is a lot of data and its partitioned into multiple CSV formatted text files. These can be huge, some ranging in 80-100mb of data.
With the data I have, I want to translate it all into a nice solid database that I can build layers on top of to use for my project. I will be using GPS positioning to pinpoint a location and all surrounding stations/stops.
My goal is to access all the information for all these stops and stations with as few calls as possible, while keeping datasets small for queried results.
I am currently leaning towards MongoDB and CouchDB for their GeoSpatial support that can really optimize getting small datasets. But I also need to be sure to link all the stops on a route because I will be propagating information along a transit route for that line. In this case I have found that I can benefit from a Graph DB like Neo4j and OrientDB, but from what I know, neither has GeoSpatial support nor am I 100% sure that a Graph DB would be what I need.
The perfect solution might not exist, but I come here asking for help on finding the best possible for my situation. I know I will possible have to work around limitations of whatever I choose, but I want to at least have done my research and know that its the best I can get at the moment.
I have also been suggested to splinter the data into multiple DBs, but that could get very messy because all the information is very tightly interconnected through IDs.
Any help would be appreciated.
Obviously a graph database fits 100% your problem. My advice here is to go for some geo spatial module over neo4j or orientdb, althought you have some others free and open source implementation.
I think the best one right now, with all the geo spatial thing implemented is neo4j-spatial package. But as far as I know, you can also reproduce most of the geo spatial thing on your own if necessary.
BTW talking about splitting, if the amount of data/queries will be high, I strongly recommend you to share the load and think the model in this terms. Sure you can do something.
I've used Mongo's GeoSpatial features and can offer some guidance if you need help with a C# or javascript implementation - I would recommend it to start because it's super easy to use. I'm learning all about Neo4j right now and I am working on a hybrid approach that takes advantage of both Mongo and Neo4j. You might want to cross reference the documents in Mongo to the nodes in Neo4j using the Mongo object id.
For my hybrid implementation, I'm storing profiles and any other large static data in Mongo. In Neo4j, I'm storing relationships like friend and friend-of-friend. If I wanted to analyze movies two friends are most likely to want to watch together (or really any other relationship I hadn't thought of initially), by keeping that object id reference I can simply add some code instructing each node go out and grab a list of movies from the related profile.
Added 2011-02-12:
Just wanted to follow up on this "hybrid" idea as I created prototypes for and implemented a few more solutions recently where I ended up using more than one database. Martin Fowler refers to this as "Polyglot Persistence."
I'm finding that I am often using a combination of a relational database, document database and a graph database (in my case this is generally SQL Server, MongoDB and Neo4j). Since the question is related to data modeling as much as it is to geospatial, I thought I would touch on that here:
I've used Neo4j for site organization (similar to the idea of hypermedia in the REST model), modeling social data and building recommendations (often based on social data). As a result, I will generally model this part of the application before I begin programming.
I often end up using MongoDB for prototyping the rest of the application because it provides such a simple persistence mechanism. I like to start developing an application with the user interface, so this ends up working well.
When I start moving entities from Mongo to SQL Server, the context is usually important - for instance, if I have an application that allows users to build daily reports based on periodically collected data, it may make sense to run a procedure that builds those reports each night and stores daily report objects in Mongo that may be combined into larger aggregate reports as needed (obviously this doesn't consider a few special cases, but that is not relevant to the point)...on the other hand, if users need to pull on-demand reports limited to very specific time periods, it may make sense to keep everything in SQL server and build those reports as needed.
That said, and this deserves more intense thought, here are some considerations that may be helpful:
I generally try to store entities in a relational database if I find that pulling an entity from the database [in other words(in the context of a relational database) - querying data from the database that provides the data required to generate an entity or list of entities that fulfills the requested parameters] does not require significant processing (multiple joins, for instance)
Do you require ACID compliance(aside:if you have a graph problem, you can leverage Neo4j for this)? There are document databases with ACID compliance, but there's a reason Mongo is not: What does MongoDB not being ACID compliant really mean?
One use of Mongo I saw in the wild that I thought was worthy of mention - Hadoop was being used to compute massive hash tables that were then stored in Mongo. I believe a similar approach is used by TripAdvisor for user based customization in terms of targeting offers, advertising, etc..
NoSQL only exists because MySQL users assume that all databases have their performance problems when their database grows large and/or becomes complex.
I suggest that you use PostGIS. You can use the same database for the rest of your data needs as well.
http://postgis.refractions.net/
Can you advise on whether I can use just the Query functionality from Lucene to generate SQL queries? Something like an SQLQueryBuilder?
I have a massive SQL database of logs from a webserver cluster containing the original request and response strings plus some other useful/less bits and bobs. What I need to do is analyse the parameters in the original request and compare with the generated responses, looking at ratios, volatility, variability, consistency etc.
This question does not relate to the analysis stage, but only the retrieval of data from database which matches the parameters I'm interested in. So, I could just do this in good old sql queries, manually building the exact queries I need on a case-by-case basis. But that's kinda lame; I reckon we can be a bit smarter than that. Particularly as I can already see large numbers of similar but subtly different queries being useful. And as I'm hoping that I can expose a single search box via a web interface to non-technical end-users, adding sql queries seems like a bad idea... and a recipe for permanent maintenance requests (and can I be the first to say, er no thanks!).
In an ideal world I expose a search form, with the option to write simple queries like
request:"someAttribute=\"someValue\"" AND response="some hoped for result" AND daterange:30
which would then hopefully find all instances of requests which contain someAttribute="someValue" over the last 30 days. The results will then be put through standard statistical analyses on the given response text and printed out on-screen. At least, that's the idea.
Much of the actual logic to determine how to handle custom field definitions or special words I'll need to write myself, and that's ok. And NB, my non-technical end users are familiar enough with xml that they can handle a bit of attr="value" syntax, at least for the first iteration of the tool :D
In summary, I want to:
1) allow users to use google-like search syntax (e.g. via Lucene's QueryAPI) to specify text to match in the logs
2) allow a layer to manipulate the query based on special words or fields (e.g. this layer could be during a Java object phase)
3) convert the final query into an sql query appropriate for my database schema
4) query the database and spit back the resultset for statistical analysis
5) pretty-print on website:)
Am I completely barking up the wrong tree? It looks like it should be possible, but I can't seem to find much on it. I've been googling for a bit on this, for example trying "Lucene SQLQueryBuilder" as a possible start but didn't really find much by way of a lead.
So, my questions are:
Has anyone tried using Lucene's QueryAPI like this before? Did it work? Any gotchas?
Are there better query api libraries out there?
Examples, finished discussions and open-source implementations would be most helpful.
Many thanks.
NB: I don't think I want Lucene's search capabilities as such, as I'm only ever looking for exact matches. I just need a query layer on top of the database.
Lucene and SQL have very little in common as they're using totally different syntax (as HefferWolf mentioned) and different underlying data models. As you said yourself, I'm afraid you're barking the wrong tree.
There are however attempts, such as Hibernate Search to bridge this gap. These are interesting experiments as such, but I would be very careful to use any of that code in production.
You could possibly use Full Text Search features available in some SQL databases, or reindex all data in Lucene and use it without database.
I doubt you can reuse any code from lucene for this. Lucene does an internal rewrite of such queries but into a syntax which wouldn't be of much help for SQL I think.
name: Phil AND lastname: Miller AND NOT age: 26
would be rewritten to
+name Phil +lastname: Miller -age: 26
So I think you would have to write your on transition into a SQL Query syntax.
But maybe you can use Lucene as such for this. Have a look into hibernate-search which is quite handy to easily create a lucene index of a sql table.
I was searching the net for something like a wiki database, just like wikipedia but instead stores structured content, editable by users. What I was looking for was an online database accessible by everyone where people can design the schema and data with proper versioning of both schema and data. I couldn't find any such site. I am not sure if it is my search skills or if there really is no wiki database as of now. Does anyone out there know anything like this?
I think there is a great potential for something like this. A possible example will be a website with a GUI for querying a MySQL DB where any website visitor can create DB objects and populate data.
UPDATE: I had registered the domain wikidatabase.org to get started on a tool but I didn't find enough time yet. If anyone is interested in spending some time and coding on this, please let me know at wikidatabase.org
It's not quite what you're looking for, but Semantic Mediawiki adds database-like features to MediaWiki:
http://semantic-mediawiki.org/wiki/Semantic_MediaWiki
It's still fundamentally a Wiki, but you can add semantic tags to pages ([[foo::bar]] [[baz::1000]]) and then do database-type queries across them: SELECT baz FROM pages WHERE foo=bar would be {{#ask: [[foo::bar]] | ?baz}}. There is even an embryonic SPARQL implementation for pseudo-SQL queries.
OK this question is old, but Google led me here, so for anyone else out there looking for a wiki for structured data: Take a look at Foswiki.
This might be like what you're looking for: dbpedia.org. They're working on extracting data from Wikipedia, and encoding it in a structured format using RDF, so that it can be queried using SPARQL.
Linkeddata.org has a big list of RDF data sets.
Do you mean something like http://www.freebase.com?
You should check out https://www.wikidata.org/wiki/Wikidata:Main_Page which is a bit different but still may be of interest.
Something that might come close to your requirements is Google Docs.
What's offered is document editing roughly similar to MS Word, and spreadsheets roughly similar to Excel. I'm thinking of the latter, of course.
In Google Docs, You can create spreadsheets for free; being spreadsheets, they naturally have a row-and-column structure similar to a database, and which you can define flexibly. You can also share these sheets with other people. This seems to be a by-invite-only process rather than open-to-all, but there may be other possibilities I'm not aware of, or that level of sharing might be enough for you in any case.
mindtouch should be able to do it. It's rather easy to get data in / out. (for example: it's trivial to aggregate all the IP's for servers into one table).
I pretty much use it as a DB in the wiki itself (pages have tables, key/value..inheritance, templates, etc...) but you can also interface with the API, write dekiscript, grab the XML...
I like this idea. I have heard of some sites that are trying to pull together large datasets for various things for open consumption, but none that would allow a wiki feel.
You could start with something as simple as an installation of phpMyAdmin with a known password that would allow people to log in, create a database, edit data and query from any other site on the web.
It might suffer from more accuracy problems than wikipedia though.
OpenRecord, development of which seems to have halted in 2008, seems to approach this. It is a structured wiki in which pages are views on the data. Unlike RDBMSes it is loosely typed - the system tries to make a best guess about what data you entered, but defaults to text when it cannot guess. Schemas appear to have been implied.
http://openrecord.org
An example of the typing that is given is that of a date. If you enter '2008' in a record, the system interprets this as a date. If you enter 'unknown' however, the system allows that as well.
Perhaps you might be interested in Couch DB:
Apache CouchDB is a document-oriented
database that can be queried and
indexed in a MapReduce fashion using
JavaScript. CouchDB also offers
incremental replication with
bi-directional conflict detection and
resolution.
I'm working on an Open Source PHP / Symfony / PostgreSQL app that does this.
It allows multiple projects, each project can have multiple directories, each directory has a defined field structure. Admins set all this up.
Then members of the public can suggest new records, edit or report existing ones. All this is moderated and versioned.
It's early days yet but it basically works and is already in real world use in several projects.
Future plans already in progress include tools to help keep the data up to date, better searching/querying and field types that allow translations of content between languages.
There is more at http://www.directoki.org/
I'm surprised that nobody has mentioned Wikibase yet, which is the software that powers Wikidata.
I have three databases that all have the contents of several web pages in them. What would be the best way to go about searching all three and having the most relevant web page at the top of the search results?
The only way I can think of is break down content by word count and/or creating a complex set of search rules to give one content priority over another. This might be more trouble than what it's worth, but I was wondering if anybody knows a way or product out there that would be able to help me.
To further support Ivans answer above Lucene is the way to go. You haven't mentioned what platform you're on so I'll point out that you can use a .NET port of this too.
If you do use Lucene there is a very good book from Manning on the subject which I recommend you look at.
When it comes to populating your index, you have a couple of choices. For starters you can just dump all of your text into the index and allow the engine to just search on it. However, I'd recommend adding fixed fields to your index which will allow you to support things such as partitioned searches or searches against those fields only.
To explain, lets say you have a field for the website. Then you can partition your index by restricting the index search to those documents that have that website in that field.
The other process is to extract points of interest from your document and allow searches on those without searching the entire index entry. Your mileage may vary with this as the lucene engine is very well written so it may simply allow you to collect your searches into more logical units which helps you with your solution.
I've done this myself and it helps when answering management questions about what exactly is searched and indexed.
HTH!
If you're using MS SQL Server then the full text search can return a ranking for you. I haven't used it, so you'll need to check the documentation or online for specifics.