Skip to main content

Analysing patent race in Hadoop area

We take a quick look at the US patents space for 2013. Compiling data from January to July 2013, we list below some of the key observations on patents which are leveraging Hadoop in their architecture.



It is interesting to note that Amazon, Xerox and IBM lead the race so far. While IBM is the usual leader in patent race, Amazon also springs no surprise since Hadoop and MapReduce have been a preferred choice of internet companies. It may be tempting to comment at Xerox’s jump among the race leaders but a closer look at data shows that the patents by Xerox were filed between 2007 and 2010 and have been published this year. It is no surprise though considering that some of the best minds have worked at Xerox and hadoopsphere had covered one of their unique architectures much earlier in the game.

Another surprising fact was thrown up on deeper digging of patent text. The following 4 patents were ditto similar on Hadoop usage text and had similar figures. That much for novelty and uniqueness! Again points out that US patent system is far from perfect. It is interesting to note that two organizations among these, Artificial Solutions and NewVoice Media have got major VC funding this year.
Description
Patent
Issued
Assignee
... delivering advanced natural language ...
US8346563
 1 Jan 2013
Artificial Solutions Ltd.
... optimized and distributed routing of ...
US8463939
 11 Jun 2013
Brian R. Galvin
... optimized and distributed resource management
US8386639
 26 Feb 2013
New Voice Media Limited
... automated testing of functionally complex ...
US8418000
 9 Apr 2013
True Metrics LLC



Looking at year on year comparison, it is clear that with growing awareness and popularity of Hadoop, the no. of patents leveraging Hadoop also has increased. Among the last year leaders, Facebook dominated the race.



Few notes:
1)      Data has been compiled from public patent search sources and not extracted directly from USPTO data. Count and numbers may vary.
2)     Usage of Hadoop has been interpreted based on available text and actual usage may vary in implemented systems.

Comments

Popular posts from this blog

Hadoop's 10 in LinkedIn's 10

LinkedIn, the pioneering professional social network has turned 10 years old. One of the hallmarks of its journey has been its technical accomplishments and significant contribution to open source, particularly in the last few years. Hadoop occupies a central place in its technical environment powering some of the most used features of desktop and mobile app. As LinkedIn enters the second decade of its existence, here is a look at 10 major projects and products powered by Hadoop in its data ecosystem.
1)      Voldemort:Arguably, the most famous export of LinkedIn engineering, Voldemort is a distributed key-value storage system. Named after an antagonist in Harry Potter series and influenced by Amazon’s Dynamo DB, the wizardry in this database extends to its self healing features. Available in HA configuration, its layered, pluggable architecture implementations are being used for both read and read-write use cases.
2)      Azkaban:A batch job scheduling system with a friendly UI, Azkab…

Data deduplication tactics with HDFS and MapReduce

As the amount of data continues to grow exponentially, there has been increased focus on stored data reduction methods. Data compression, single instance store and data deduplication are among the common techniques employed for stored data reduction.
Deduplication often refers to elimination of redundant subfiles (also known as chunks, blocks, or extents). Unlike compression, data is not changed and eliminates storage capacity for identical data. Data deduplication offers significant advantage in terms of reduction in storage, network bandwidth and promises increased scalability.
From a simplistic use case perspective, we can see application in removing duplicates in Call Detail Record (CDR) for a Telecom carrier. Similarly, we may apply the technique to optimize on network traffic carrying the same data packets.
Some of the common methods for data deduplication in storage architecture include hashing, binary comparison and delta differencing. In this post, we focus on how MapReduce and…

Top Big Data Influencers of 2015

2015 was an exciting year for big data and hadoop ecosystem. We saw hadoop becoming an essential part of data management strategy of almost all major enterprise organizations. There is cut throat competition among IT vendors now to help realize the vision of data hub, data lake and data warehouse with Hadoop and Spark.
As part of its annual assessment of big data and hadoop ecosystem, HadoopSphere publishes a list of top big data influencers each year. The list is derived based on a scientific methodology which involves assessing various parameters in each category of influencers. HadoopSphere Top Big Data Influencers list reflects the people, products, organizations and portals that exercised the most influence on big data and ecosystem in a particular year. The influencers have been listed in the following categories:

AnalystsSocial MediaOnline MediaProductsTechiesCoachThought LeadersClick here to read the methodology used.

Analysts:Doug HenschenIt might have been hard to miss Doug…