Big thinking

In the field of big data, leading-edge technologies are helping ease the problems inherent to working with large and valuable mines of information, providing new ways to reuse and extract value from them.

Keen to build an industrial community around this ecosystem, the European Commission (EC) is co-funding a two-year project, baptised the Big Data Public Private Forum (BIG).

Active since September 2012, the initiative is also working to establish the necessary collaboration and dissemination infrastructure to link technology suppliers, integrators, and leading user organisations.

The main objectives of the group, which is made up of 11 companies, research institutions, and community initiatives from six European countries are as follows:

  • to provide a clear picture of existing technology trends, and their maturity
  • acquire a sharp understanding of how big data can be applied to concrete environments
  • push big data research at the European level, as well as innovation, to contribute towards increasing European competitiveness, and
  • to build a self-sustainable, industry-led initiative

Activities are structured around a matrix, whereby technical specialists and sectorial experts collaborate in order to foster cross-fertilisation during the project run time, and beyond. That is:

  • Sectoral forums: Health; Public Sector; Finance & Insurance; Telecommunication; Media & Entertainment; Manufacturing; Retail, Energy & Transport
  • Technical working groups: Data acquisition, analysis, curation, storage, and usage

All of the above work is conducted online (through mailing lists, webinars, and telecons), as well as at workshops, conventions, and conferences. Furthermore, the BIG Evidence Hub is an online venue for the project community to pool related challenges, issues, requirements, technologies, and concepts.

“The ubiquitous availability of the internet as a medium for data collection, aggregation, and distribution enables completely novel, data­driven applications and usage scenarios,” comments Sören Auer, from project partner University of Leipzig.

Big data, bigger business?

Alongside other industries, the railways are discovering how this data-based innovation can help boost their business.

In essence, it is enabling them to improve fleet efficiency and capacity through time and energy management, remote monitoring, preventive maintenance, and adaptive analytics.

Applications are wide ranging, and significant.

Automatic passenger counting calculates the number of riders boarding or alighting vehicles, then streams the information captured to a central database. Automatic fare collection, route planning, scheduling and vehicle location are further examples. And there are plenty more…

Sooner rather than later – the intelligent approach to maintenance

An example of big data in action comes from the Taiwan High Speed Rail (THSR) line, running commercially down the west coast of the island since 2007.

Operated by the Taiwan High Speed Rail Corporation (THSRC), the fully electrified system is served by Japanese bullet trains (the THSR 700T model, built primarily on the back of the 700 series Shinkansen; the ‘T’ refers to Taiwan).

A non-stop ride enables passengers to make the 214-mile trip from Taipei in the north to Kaohsiung in the south in just 90 minutes; a journey that takes 4.5 hours by road.

Given the high-speed status of the rolling stock and infrastructure, their maintenance requirements are under considerable pressure. To meet them, THSRC has used Maximon, a software by IBM, to build an advanced equipment maintenance solution.

This Maintenance Management Information System (MMIS) is capable of automatically triggering maintenance activities by detecting potential problems in the network, and, through automatic alarms, of addressing the problem sooner, rather than later, i.e. before putting passenger safety at risk.

The MMIS gathers data from existing monitoring and telemetry systems (such as SCADA, signalling systems, and rolling stock sensors), and integrates it into a simplified planning and maintenance workflow.

It currently gathers over 320,000 data elements – from the rotation and temperature of wheels to the thickness of the catenary cable. In the case of the train wheel, for instance, condition-based data is sent wirelessly (in real time) to a central repository, for comparison with normal specifications.

Improving mobility, boosting economic growth

Thanks to the MMIS, Ming-Der Lee at THSRC is confident Taiwan’s HS service is well-positioned to fulfill its role within the national strategy for reducing congestion, improving intercity mobility, and boosting the economy.

Yet passenger safety has officially not been overlooked. “With IBM’s Maximo, we’ve succeeded in building an intelligent transportation system designed to meet the growing demand for safe, high-speed travel in Taiwan,” says Mr Lee.

He also sees the MMIS as a key underpinning of the company’s record of punctuality: 99.15% of train arrivals and departures are within six seconds of the scheduled times.

“IBM’s software has helped us to gain greater insight into the condition of our assets, to develop more efficient work processes, and to stay one step ahead of maintenance issues,” he explains.

Over 95% of THSRC’s maintenance work orders are generated and managed through the system.

At the Big Data Crossroads: towards a smarter travel experience

In a white paper published in 2013, Amadeus explores how rail travel could be transformed by big data. With regards to changes in the operational side of business, it suggests these will come from sensors installed in large transport devices:

‘General Electric (GE), for example, is aggressively placing sensors in jet engines, hoping that the data from them will allow both more efficient operations and more timely maintenance.

‘It hopes to capture the resulting data to better optimise its own service contracts, and the businesses of its travel provider customers. Even small benefits provide a large payoff when adopted on a large scale.

General Electric estimates that a 1% fuel reduction in the use of big data from aircraft engines would result in a $30 billion [€22 billion] saving for the commercial airline industry over 15 years.’

Let’s look to another industry sector for further big data inspiration, as referenced in the Amadeus paper:

‘Energy consumption is also an important focus of big data in hotels. With new providers of ‘smart grid’ and energy management services coming on stream, substantial new opportunities will emerge.

‘For example, two San Francisco InterContinental hotels are already using capabilities from Stem, a big data for energy management start-up.

‘Stem’s software gathers data from more than 50 different sources — including weather data, electricity rates and a building’s energy consumption — to build a comprehensive building energy profile. Through a cloud-based, predictive analytics algorithm, the software can fine-tune whether power comes from the grid or an onsite battery module. InterContinental’s management expects to reduce energy costs by 10 to 15%.’

Putting heads together

There are two schools of thought on the much hyped big data movement:

  • Some say it is simply a trend, whereby existing products and solutions are being relabelled
  • Others believe it really does describe the emerging and maturing technologies coming to market.

Wherever the truth may lie (probably between the two), nobody can argue that data sets are accumulating in every business activity.

But to truly reap the benefits, solution suppliers and their clients must put their heads together and think smart, i.e. clearly identify and fully understand the needs in questions.

“The impact of open data grows as an ever increasing amount, depth, and diversity of information is made available on the Web,” says Pablo Mendes, from the Open Knowledge Foundation Deutschland (a BIG partner). “But its highest value can only be attained with adequate methods to store, analyse, and effectively interpret those data.”

‘Big data is being generated by everything around us at all times. Every digital process and social media exchange produces it. Systems, sensors, and mobile devices transmit it.

‘Big data is arriving from multiple sources at an alarming velocity, volume, and variety. To extract meaningful value from big data, you need optimal processing power, analytics capabilities and skills.’

Source: IBM

 

Cover photo source: loop_oh

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