Fujitsu Develops World's First Stream Aggregation Technology to

Fujitsu Develops World's First Stream Aggregation Technology to Rapidly Process
Both Historical Data and Incoming Data 
Kawasaki, Japan, Nov 19, 2012 - (JCN Newswire) - Fujitsu Laboratories Limited
announced development of the world's first stream aggregation technology
able to rapidly process both stored historical data and incoming streams of new
data in a big data context. 
The nature of big data requires that enormous volumes of data be processed at
a high speed. When data is aggregated, longer aggregation times result in
larger data volumes to be processed. This means computation times lengthen,
which causes frequent updating operations to become more difficult. This is why
improving the frequency of updates when aggregation times are lengthened has so
far been challenging. Fujitsu Laboratories has therefore developed a technology
that returns computation results quickly and manages snapshot operations,
without re-doing computations or re-reading a variety of data types that change
over time. As a result, even with high-frequency updating and long aggregation
times, data can be processed 100 times faster than before. 
This technology promises to improve both large volumes of batch processing and
the processing of streaming data. Furthermore, in meteorology, it is now
possible to show concentrated downpours in specific areas. As well as the
utility gained for future weather forecasting, it may also have uses in new
fields that demand the ability to process longitudinal data in real time. 
Details of this technology will be announced at a special workshop lecture of
the Special Interest Group on Software Interprise Modeling (SWIM) of the
Institute of Electronics, Information and Communication Engineers (IEICE) held
on Friday, November 30, at the Takanawa campus of Tokai University in Japan. 
Many companies are interested in using advanced ICT technology to improve
their competitive position by rapidly processing large volumes of data. Some
uses are large-scale batch processes performed periodically on transaction
data, or processing streaming data in real time based on changing stock
In the data processing of such activities, aggregating computations is
essential. In large-volume batch processing, however, there are differences in
the aggregation times and update frequency. Typically, large-volume batch
processes that emphasize throughput operate on aggregation times lasting weeks
or months. Streaming data processes emphasize response, on the other hand, and
are in units of seconds or minutes. Update times roughly correspond with
Technological Challenges 
The emphasis on batch processes and streaming processes is different, and
therefore the process needs to be adapted according to application. 
1. Large-volume batch processing technology 
Large-volume batch processing handles large volumes of historical data, so
each round of processing re-reads all data, which creates long delays before
results are ready. 
2. Conventional stream processing technology 
The constant flow of data is held in a buffer - known as a window - and
therefore each round of processing does not need to re-read any earlier data.
Depending on the type of computation, however, the process does need access to
all the data in that window in order to obtain computation results. For this
reason, the duration of one round of computations will be proportionate to the
window length, which diminishes responsiveness. 
When using both historical (stored) and current (realtime streaming) data,
with conventional processing methods, it has been difficult to simultaneously
lengthen the aggregation intervals and raise the frequency of updates for the
reasons outlined above. 
Newly Developed Technology 
Fujitsu Laboratories has developed a fast stream aggregation technology for
long aggregation intervals and frequent updates, based on a combination of the
two technologies described below. 
1. Rapid pattern matching technology: 
This is a technology that efficiently and directly picks out relevant items
from an incoming stream of data. The conventional technique begins by analyzing
the structure of input data and temporarily accumulating all input data in the
memory. Next, it performs an extraction process of the items needed for
aggregation to extract data. Structural analysis and item extraction is
necessarily a two-step process. This technology is different in that it
specifies the positions where items to be extracted will appear based on
pattern matching, skipping over unneeded items thereby speeding up the process.
Also, because pattern-matching is flexible, as well as using it with
fixed-format data (such as CSV data) that conventional techniques use, it can
work with other forms of data having recursive or hierarchical structures (such
as XML data). 
2. Snapshot operation management technology: 
This is a technology that quickly returns computation results to deal with a
variety of data types that change over time, without re-reading or re-computing
data. The conventional technique is to store in memory an incoming stream of
data following its time sequence. This technology stores the data even as it
performs required computations, such as sorting according to a predefined
order. It is always managed based on its computed state (snapshot operation),
and therefore never needs to redo computations that involve all the data,
including not only sums and averages but also minima, maxima, and medians. This
lets it quickly pick out computation results. 
The response time for aggregation results when using a window length of
500,000 records was shown to be roughly 100 times faster than the commonly used
open-source Complex Event Processing engine. It was also demonstrated that
response time does not depend on window length (Figure 3). 
This technology is expected to have applications with regard to the
utilization of high-precision sensor data. Fujitsu Laboratories conducted
verification of the technology using rainfall data generated by XRAIN(1), a
project conducted by the Water and Disaster Management Bureau of the Ministry
of Land, Infrastructure, Transport and Tourism. In the case of aggregating
rainfall volume data collected over several hours from 500,000 locations in the
Kansai region of western Japan, every several minutes a window of approximately
100 million records needs to be processed. The test conducted by Fujitsu
Laboratories confirmed the technology's ability to execute data
aggregation within intervals and no variation in aggregation times, and that
the smooth movement of the rainfall area could be replicated, even for such a
wide range of data. More than a sudden downpour, the actual volume of rainfall
is what is strongly associated with disasters, and now, areas that require
vigilance due to concentrated downpours can be readily verified. 
Moreover, applications are anticipated for existing batch processing and
stream processing. By enhancing the real-time aggregation of sales data, for
example, it becomes possible to further strengthen production and inventory
Future Plans 
Fujitsu plans to incorporate the new technology into its Big Data Platform and
Big Data Middleware in fiscal 2013. 
(1) Rainfall data generated by XRAIN:Rainfall data generated by the X-band MP
Radar Rainfall Data, or XRAIN project, conducted by the Ministry of Land,
Infrastructure, Transport and Tourism. XRAIN seeks to maintain extremely
localized weather data, capturing rainfall data every 250 meters at one-minute
intervals over a wide area. 
About Fujitsu Laboratories 
Founded in 1968 as a wholly owned subsidiary of Fujitsu Limited, Fujitsu
Laboratories Limited is one of the premier research centers in the world. With
a global network of laboratories in Japan, China, the United States and Europe,
the organization conducts a wide range of basic and applied research in the
areas of Next-generation Services, Computer Servers, Networks, Electronic
Devices and Advanced Materials. For more information, please see: 
About Fujitsu Limited 
Fujitsu is the leading Japanese information and communication technology (ICT)
company offering a full range of technology products, solutions and services.
Over 170,000 Fujitsu people support customers in more than 100 countries. We
use our experience and the power of ICT to shape the future of society with our
customers. Fujitsu Limited (TSE:6702) reported consolidated revenues of 4.5
trillion yen (US$54 billion) for the fiscal year ended March 31, 2012. For more
information, please see 
Fujitsu Limited
Public and Investor Relations
Technical Contacts 
Fujitsu Laboratories Ltd.
Software Systems Laboratories
Intelligent Technology Lab
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