Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

August 20, 2011

May the Index be with you!

Filed under: MySQL,Query Language,SQL — Patrick Durusau @ 8:06 pm

May the Index be with you! by Lawrence Schwartz.

From the post:

The summer’s end is rapidly approaching — in the next two weeks or so, most people will be settling back into work. Time to change your mindset, re-evaluate your skills and see if you are ready to go back from the picnic table to the database table.

With this in mind, let’s see how much folks can remember from the recent indexing talks my colleague Zardosht Kasheff gave (O’Reilly Conference, Boston, and SF MySQL Meetups). Markus Winand’s site “Use the Index, Luke!” (not to be confused with my favorite Star Wars parody, “Use the Schwartz, Lone Starr!”), has a nice, quick 5 question indexing quiz that can help with this.

Interesting enough to request an account so I could download ToKuDB v.5.0. Uses fractal trees for indexing speed. Could be interesting. More on that later.

August 1, 2011

UnQL

Filed under: JSON,Query Language — Patrick Durusau @ 3:51 pm

UnQL

From the webpage:

UnQL means Unstructured Query Language. It’s an open query language for JSON, semi-structured and document databases.

Another query language. Thoughts?

June 27, 2011

Introduction to Cypher

Filed under: Cypher,Neo4j,Query Language — Patrick Durusau @ 6:37 pm

Introduction to Cypher

From the webpage:

Michael Hunger introduces basic graph queries on a movie dataset using Neo4j’s Cypher language.

Short but impressive! Show this one to anyone you want to convince about using graph databases.

Scalable Query Processing in Probabilistic Databases

Filed under: Probabilistic Database,Probabilistic Programming,Query Language — Patrick Durusau @ 6:34 pm

Scalable Query Processing in Probabilistic Databases

From the webpage:

Today, uncertainty is commonplace in data management scenarios dealing with data integration, sensor readings, information extraction from unstructured sources, and whenever information is manually entered and therefore prone to inaccuracy or partiality. Key challenges in probabilistic data management are to design probabilistic database formalisms that can compactly represent large sets of possible interpretations of uncertain data together with their probability distributions, and to efficiently evaluate queries on very large probabilistic data. Such queries could ask for confidences in data patterns possibly in the presence of additional evidence. The problem of query evaluation in probabilistic databases is still in its infancy. Little is known about which queries can be evaluated in polynomial time, and the few existing evaluation methods employ expensive main-memory algorithms.

The aim of this project is to develop techniques for scalable query processing in probabilistic databases and use them to build a robust query engine called SPROUT ( Scalable PROcessing on Tables). We are currently exploring three main research directions.


  • We are investigating open problems in efficient query evaluation. In particular, we aim at discovering classes of tractable (i.e., computable in polynomial time wrt data complexity) queries on probabilistic databases. The query language under investigation is SQL (and its formal core, relational algebra) extended with uncertainty-aware query constructs to create probabilistic data under various probabilistic data models (such as tuple-independent databases, block-independent disjoint databases, or U-relations of MayBMS).

  • For the case of intractable queries, we investigate approximate query evaluation. In contrast to exact evaluation, which computes query answers together with their exact confidences, approximate evaluation computes the query answers with approximate confidences. We are working on new techniques for approximate query evaluation that are aware of the query and the input probabilistic database model (tuple-independent, block-independent disjoint, etc).

  • Our open-source query engine for probabilistic data management systems uses the insights gained from the first two directions. This engine is based on efficient secondary-storage exact and approximate evaluation algorithms for arbitrary queries.

As of June 2, 2011, order Probabilistic Databases by Dan Suciu, Dan Olteanu, Christopher Re, and Christoph Kock from Amazon.

Exciting work!

It occurs to me that semantics are always “probabilistic.”

What does that say about the origin of the semantics of a term?

If semantics are probabilistic, is it ever possible to fix the semantic of a term?

If so, how?

June 18, 2011

Do you like it rough?

Filed under: Query Language — Patrick Durusau @ 5:46 pm

Infobright Rough Query: Aproximating Query Results
by Alex Popescu.

Interesting post about Infobright’s “rough queries” that return a range of data, which can be mined with more specific queries.

Makes me wonder about the potential for “rough” merging that creates sets of similar subjects, which are themselves subject (sorry) to further refinement. Depends on the amount of resources you want to spend on the merging process.

One level could be that you get the equivalent of a current typical search engine result. Most of it maybe relevant to something, maybe even your query, but who wants to slog through > 10,000 “hits.”

The next level could be far greater refinement that gets you down to relevant “hits” in the 1,000 range. With a following level of 100 “hits.”

The last level could be an editorial piece with transcluded information from a variety of sources and links to more information. Definitely an editorial product.

Price goes up as the amount of noise goes down.

May 13, 2011

SPARQL 1.1 Drafts – Last Call

Filed under: Query Language,RDF,SPARQL — Patrick Durusau @ 7:19 pm

SPARQL 1.1 Drafts – Last Call

From the W3C News:

May 5, 2011

SPARQL by Example
(with Cheatsheet)

Filed under: Query Language,SPARQL — Patrick Durusau @ 1:45 pm

SPARQL by Example

SPARQL by Example: The Cheatsheet

Good introductory materials.

Recall that MaJorToM and Maiana both support SPARQL queries.

April 29, 2011

Horton: online query execution on large distributed graphs

Filed under: Graphs,Networks,Query Language,Social Graphs — Patrick Durusau @ 1:12 pm

Horton: online query execution on large distributed graphs by Sameh Elnikety, Microsoft Research.

The presentation addresses three problems with large, distributed graphs:

  1. How to partition the graph
  2. How to query the graph
  3. How to update the graph

Investigates a graph query language, execution engine and optimizer, and concludes with initial results.

January 21, 2011

Feldspar: A System for Finding Information by Association

Filed under: Associations,Query Language,TMQL,Visual Query Language — Patrick Durusau @ 5:28 pm

Feldspar: A System for Finding Information by Association

…use non-specific requirements to find specific things.

Uses associations to build queries.

Associations developed by Google Desktop.

Very cool!

January 17, 2011

Rogue

Filed under: MongoDB,Query Language,Scala — Patrick Durusau @ 8:38 pm

Rogue

From the website:

Rogue is a type-safe internal Scala DSL for constructing and executing find and modify commands against MongoDB in the Lift web framework. It is fully expressive with respect to the basic options provided by MongoDB’s native query language, but in a type-safe manner, building on the record types specified in your Lift models.

Seen on MyNoSQL

*****
PS: To learn more about Lift, see: http://liftweb.net/

January 7, 2011

Provenance for Aggregate Queries

Filed under: Aggregation,Merging,Query Language,TMQL — Patrick Durusau @ 7:19 am

Provenance for Aggregate Queries Authors: Yael Amsterdamer, Daniel Deutch, Val Tannen

Abstract:

We study in this paper provenance information for queries with aggregation. Provenance information was studied in the context of various query languages that do not allow for aggregation, and recent work has suggested to capture provenance by annotating the different database tuples with elements of a commutative semiring and propagating the annotations through query evaluation. We show that aggregate queries pose novel challenges rendering this approach inapplicable. Consequently, we propose a new approach, where we annotate with provenance information not just tuples but also the individual values within tuples, using provenance to describe the values computation. We realize this approach in a concrete construction, first for “simple” queries where the aggregation operator is the last one applied, and then for arbitrary (positive) relational algebra queries with aggregation; the latter queries are shown to be more challenging in this context. Finally, we use aggregation to encode queries with difference, and study the semantics obtained for such queries on provenance annotated databases.

Not for the faint of heart reading.

But, provenance for merging is one obvious application of this paper.

For that matter, provenance should also be a consideration for TMQL.

December 12, 2010

SRU Search/Retrieval via URL

Filed under: Library,Query Language,Retrieval — Patrick Durusau @ 8:00 pm

SRU Search/Retrieval via URL

Standards, resources, including free implementations for the SRU effort.

SRU: the protocol – SearchRetrieve Operation: Binding for SRU 2.0 (draft)

CQL: The Contextual Query Language – CQL: The Contextual Query Language (draft)

The website reports that standardization is to be completed soon. And the available drafts date from 2010.

However, if you follow known servers you will find only thirteen (13) known servers as of 12 December 2010.

Standards can be written prior to wide spread adoption but before spending too much effort on this protocol and query language, I think we need to watch its adoption curve closely.

December 3, 2010

Neo4j 1.2 Milestone 5 – Reference Manual and HA! – Post (Protends for TMQL?)

Filed under: Graphs,Neo4j,Query Language,TMQL — Patrick Durusau @ 9:27 am

Neo4J 1.2 Milestone 5 – Reference Manual and HA!

News of the release of a reference manual for Neo4j and a High Availability option (the HA in the title).

I know it is a reference manual but I was disappointed there was no mention of topic maps.

Surprising I know but it still happens. 😉

Guess I need to try to find the cycles to generate, collaborate on, etc., some documentation that can be posted to the topic maps community for review.

Assuming it passes muster there, it can be passed along to the Neo4j project.

BTW, I found a “related” article listed for Neo4j that starts off:

A multi-relational graph maintains two or more relations over a vertex set. This article defines an algebra for traversing such graphs that is based on an $n$-ary relational algebra, a concatenative single-relational path algebra, and a tensor-based multi-relational algebra. The presented algebra provides a monoid, automata, and formal language theoretic foundation for the construction of a multi-relational graph traversal engine.

Can’t you just hear Robert saying that with a straight face? 😉

Seriously, if we are going to compete with enterprise grade solutions, that is the level of thinking that needs to underlie TMQL.

It is going to require effort on all our parts but “good enough” solutions aren’t and should not be supported.

November 21, 2010

Ontology Based Graphical Query Language Supporting Recursion

Filed under: Ontology,Query Language,Semantic Web,Visual Query Language — Patrick Durusau @ 7:55 am

Ontology Based Graphical Query Language Supporting Recursion Author(s): Arun Anand Sadanandan, Kow Weng Onn and Dickson Lukose Keywords: Visual Query Languages, Visual Query Systems, Visual Semantic Query, Graphical Recursion, Semantic Web, Ontologies

Abstract:

Text based queries often lead tend to be complex, and may result in non user friendly query structures. However, querying information systems using visual means, even for complex queries has proven to be more efficient and effective as compared to text based queries. This is owing to the fact that visual systems make way for better human-computer communication. This paper introduces an improved query system using a Visual Query Language. The system allows the users to construct query graphs by interacting with the ontology in a user friendly manner. The main purpose of the system is to enable efficient querying on ontologies even by novice users who do not have an in-depth knowledge of internal query structures. The system also supports graphical recursive queries and methods to interpret recursive programs from these visual query graphs. Additionally, we have performed some preliminary usability experiments to test the efficiency and effectiveness of the system.

From the abstract I was expecting visual representation of the subjects that form the query. The interface remains abstract but is a good step in the direction of a more useful query interface for the non-expert. (Which we all are in some domain.)

Questions:

  1. Compare to your experience with query language interfaces. (3-5 pages, no citations)
  2. Are recursive queries important for library catalogs? (3-5 pages, no citations, but use examples to make your case, pro or con)
  3. Suggestions for a visual query language for the current TMQL draft? (research project)
« Newer Posts

Powered by WordPress