reference data

URLs – Following the Trail of Associative Thinking

URLs – Following the Trail of Associative Thinking

We all understand that the human mind does not operate in a linear fashion. Despite this, people routinely rely on linearly arranged materials (reports, spreadsheets, articles, etc.) to review or study new information. Such materials are often organized into sections on individual topics, with each section consisting of paragraphs featuring a specific idea (effectively building a plodding, linear structure). This is antithetical to the associative nature of the human mind. It is only natural, then, that throughout history we have dreamt of machines that would one day allow us to review information and data in multilinear and tangential fashions.

“As We May Think” Is How We Really Think

Memex Machine

Bush’s Memex machine as visualized in the original print publication in The Atlantic.

In 1945, Vannevar Bush published his renowned article, “As We May Think,” in which he discusses the associative nature of the human mind. Bush also wrote at length about the inadequate structure of data storage and his vision for a machine, the Memex (Memory Extender) that would mirror the associative qualities of the human mind while also relieving people of the burden of scouring through endless indexes for information. Bush maintained that the human mind “operates by association” and that once we grasp an idea, our minds “snap instantly to the next [idea] that is suggested by the association of thoughts, in accordance with some intricate web of trails carried by the cells of the brain.”

Bush’s admiration for the power of the human mind is evident from the following quote: “the speed of action, the intricacy of trails, the detail of mental pictures, is awe-inspiring beyond all else in nature.” It is natural, then, that Bush would propose the building of a machine that would mimic the abilities of the mind. Bush envisioned the Memex as operating with the speed and associative capBush Pull quoteability of the mind, in other words, a machine that would literally become an extension of memory and thought, stating that “selection by association, rather than indexing, may yet be mechanized.” Bush envisioned the mechanization of association by having the user of the Memex “build a trail” or mind map. This process of joining or linking information was an early call for the need and capabilities of URLs.

Data Pointers

There are many disadvantages to traditional data storage – one of the biggest disadvantages being that users must rely on complicated queries to search through linear, tabular data in order to find specific information. Computer memory is even more rudimentary – a contiguous string of seemingly meaningless zeroes and ones. In order to make use of computer memory, natural information gets digitized, transformed programmatically into appropriate data structures and stored as memory. Once this is accomplished, data can later be retrieved and interpreted as program logic at a location address or pointer which must also be memorized.

Data pointers have been used for years (since 1964 to be exact) to improve data retrieval and to help programmers ruminate about data at a higher level of abstraction. A pointer is a value that references or points to another value stored somewhere else within a program memory. Essentially, acting as something of a signpost, allowing users to more easily find the data they want to review. The obvious downside to this method is that the data pointer is limited to data contained within one machine – making building distributed systems problematic.

SlashDB as Your Innovative Solution

SlashDB technology takes the concSlashDB Logoept of data pointers to the next level by using Uniform Resource Locators (URLs) as pointer structured data resources. SlashDB automatically assigns each data resource a URL which allows data to be sourced from one machine to another. This not only helps users navigate data and associate specific pieces of data more easily than ever before, but also allows software architects to think of disparate data in similar terms as if that data were contained within the program’s memory.

For instance, a URL data pointer for a customer table will lead to a pointer for a specific customer within that table, which will, in turn, lead to a specific property of the customer, such as an email address or invoices. Data exposed at this granular level, like small breadcrumbs dropped along a logical path of thought, provide a trail or map that allows programmers to build applications spanning multiple machines. URL data pointers coupled with SlashDB technology sync seamlessly with thought processes and patterns, allowing URLs to perfectly imitate the highly associative nature of the human mind and memory.

SlashDB has made Bush’s concept a reality, and, in doing so, takes his vision of associative technology one step further by creating unique data pointers in the form of URLs for each piece of data – providing associative footholds for the mind to use with the greatest ease possible. SlashDB thoroughly understands that tools which share the same associative capabilities of the mind increase utilization and heighten productivity.

Mind Map

Mind Map

Associative technology melds with the mind, allowing for a rapidity of exploration based on association. This results in an intricate network of relationships that can range from the highly related to the tangential to the most tenuous of connections, all of which can work to create new and novel conceptions of data and data usage.

SlashDB has made it our mission to make data retrieval a highly accessible, searchable, and associative process.  If you think our innovative methods will help streamline your business, contact us and we’ll work with you to find the best solution for your needs.

 

How Bloomberg Uses REST APIs

How Bloomberg Uses REST APIs

Bloomberg Industry Leaderboard Uses REST API for Financial Data Visualization; Imagine What You Could be Doing with Your Data Assets

If you have visited Bloomberg’s website lately you may have noticed a new tool called Bloomberg Industry Leaderboard, which is a part of their Visual Data site. The Leaderboard presents fundamental data about 600 leading global corporations in a visually attractive manner. Visualization techniques such as tree map, colored grid and rankings are all dynamically configurable, and results are sorted on the fly.

bloomberg-leaderboard

 

While the concept of presenting fundamental metrics in similar ways is not new, and there are many websites with similar data and visualization, the technical details behind the site are worth examining a bit closer.

Traditionally, data-driven  web pages respond to users input (clicks) by requesting from the web server a fully prepared page, coded in HTML for the browser to render. This typically results with reloading of the entire page upon each interaction or (more recently) with replacing fragments of existing HTML with new ones. In contrast, Bloomberg’s site uses REST/HTTP API to get raw data, which the browser then combines with a shell HTML page using Javascript and Cascading Style Sheets.

For us what is even more interesting is that Bloomberg seems to follow a very similar approach to that of SlashDB. Here’s an example of companies broken down industry and ranked by operating margin and estimated net income growth:

http://www.bloomberg.com/visual-data/industries/rank/margin:est-net (HTML representation)
http://www.bloomberg.com/visual-data/industries/db/rank/margin:est-net.json (underlying data)

By comparison, SlashDB links (to an unrelated data set) look as follows:

http://demo.slashdb.com/db/Chinook/Customer/Country/Brazil.html (HTML representation)
http://demo.slashdb.com/db/Chinook/Customer/Country/Brazil.json (underlying data)

Imagine what you could do by layering SlashDB on top of your data. Use it internally for data federation, database search and self-service reporting, or deliver data to the web and mobile apps, or even offer your data assets for sale. Either way, the time to market is about an order of magnitude shorter than custom developed solutions, as our customers have attested. SlashDB is also more versatile as it allows for reading and writing of data and provides alternative data formats. It just as easily integrates with Excel, R, Matlab and enterprise systems as it does with the web.

As you may know, the idea for SlashDB was conceived out of the issues with access to market data in large investment banks. Had Bloomberg used SlashDB, they could have saved a ton of time and money.

Try /db Risk Free

If you would like to learn more about SlashDB or to discuss REST APIs in finance or in general, please contact us. You can also register here to try our product risk free.

SlashDB Among Tech Partners to the Fintech Hackathon

Come hack with us April 6-7, 2013 at the Fintech Hackathon in NYC. Win $10,000 + exposure to thought leaders in the industry.

Fintechhack_small_logo

SlashDB is pleased to be a technology partner to the event among esteemed financial and web technology businesses such as: 10gen, Bloomberg, Caplin, CardFlight, Disqus, Dwolla, Estimize, Kaazing, Oanda, OpenFin, OpenGamma, StockTwits, Tradable, Xignite and Zipmark. Special thanks to Nick Gavronsky from OpenFin for inviting us to participate.

Although SlashDB has grown to become an industry agnostic database-to-web “middleware” the idea for it was actually conceived out of problems with data management observed in large financial institutions.

As you may know, SlashDB can create a mesh of all your database resources accessible in the same way, by referencing URLs. We call that a Resource Oriented Architecture (ROA). With the ROA approach there is far less of a need to use ETL processes and data warehouses. Instead of copying data, use a reference to data.

Data transparency and a capability for instant error correction naturally stem from this approach. Also, the fact you can use the same link to power an in-house trade processing service or a risk dashboard on a mobile device or even desk quant’s Excel workbook is for some just too good to be true. Except it is true and will become a norm.

SlashDB instance running on Amazon Elastic Cloud will be made available to the hackathon participants and will support any Amazon RDS databases (SQL Server, MySQL, Oracle) as well as SQLite files. If you have any questions for us ahead of the event, please send them to finhack @ slashdb.com.

We hope to see you there! Don’t delay, register now.

Turn any Database into an Online Resource with Assetcloud Powered by /db

Turn any Database into an Online Resource with Assetcloud Powered by /db

The news is outNovember, 14-15 we will be exhibiting /db at the NY Business Expo, booth #66 at the 69th Regiment Armory in New York City. Come join us, see /db in action.

Turn Any Database into an Online Resource with VT Enterprise Assetcloud Powered By /db

Financial industry first to adopt Assetcloud powered by /db to cope with data silos, become cloud-ready. Automatically generated REST API streamlines enterprise data management and paves the way for mobile enterprise applications.

Jersey City, NJ (PRWEB) November 06, 2012

VT Enterprise announces the immediate availability of Assetcloud powered by /db, a solution to the growing data access problem in financial industry. As institutions become increasingly information-driven, databases play a crucial role in driving critical business processes. However, organizations often encounter multiple databases in different departments that are hard to access. Assetcloud powered by /db solves this data management problem by instantly turning any group of databases into a “cloud” of online data resources that are easily searchable and accessible by authorized users and applications. [Read more]

Resource Oriented Architecture Among Solutions to Data Silos

Resource Oriented Architecture Among Solutions to Data Silos

Inside Reference Data magazine in article “Transparent Demands” by Michael Shashoua refers to our opinion on using Resource Oriented Architecture to combat data silos and achieve greater data transparency. Subscription is required to read but free trial is available or see the snippet below.

Dismantling Data Silos

Services such as Bloomberg’s BVAL, launched about two years ago, and others emulating BVAL’s capabilities are proving beneficial to buy-side firms to save time, meet regulatory requirements for pricing data and generally do their jobs, according to Smith.

The more sources end-users have, especially from individuals trading the security in question, the greater likelihood of getting transparent and vetted prices, he explains. “If you have a whole bunch of dealers who trade a security, and you can get them all in one spot, in real time or at least a couple times a day, you’ll have a much better idea and more confidence where that’s trading,” says Smith. “Some vendors are better than others at it.”

However, with multiple sources, data silos present an obstacle to achieving transparency, according to Victor Olex, founder and president of VT.Enterprise, a Jersey City, N.J.-based provider of financial industry technology. Data-sharing methods including extract-transform-load processes (ETL), service-oriented architecture (SOA) and data warehousing all fail to address the problems created by silos, adds Olex.

VT.Enterprise uses “resource-oriented architecture” (ROA) which allows data to remain in a user’s original systems while offering a central, on-demand and uniform access mechanism to all data stores, in interoperable fashion, under standard data formats, according to Olex.