What can you expect from a Smart Stadium?

As home entertainment systems improve (e.g. 3D HD TV), stadiums are coming under increased pressure to differentiate themselves and offer a pleasing entertainment environment for their fans and visitors. 

To continue to attract visitors, there is a need to address the challenges of managing traffic flows,  parking, the need for collaboration with public safety officials (police, fire, ambulance) etc.

The most progressive stadiums are investing in technology that can be used to improve the operation of their facilities, essentially enabling them to create a Smarter Stadium.

The ability to monitoring gate throughput can result in a better understanding of how fast the stadium is filling, enabling operations staff to optimize the flow of traffic into and out of the stadium, as well as within the grounds. Data from the turnstiles can be aggregated into a dashboard and analyzed against key performance indicators. Operators can use dashboards to view the flow rate across each gate, and if the traffic becomes too high, they can take corrective action by either slowing the flow further downstream, or diverting traffic to other gates with more capacity. As fans drive into the stadium, they could be notified by SMS to park at an alternative location to optimize traffic flow into the stadium.

Performance of concession stands can also be monitored to understand how each is performing. Monitoring sales channels and offers performance should give a good insight into POS revenue and concourse level revenue, making it possible for decisions to be taken in realtime to improve sales performance on the fly. Data could be aggregated further into concession location revenue and terminal revenue to analyze and understand buying behavior and trends across different parts of the stadium. Sales transaction information could also be combined with other queue monitoring systems to offer realtime navigation assistance to stalls with the shortest queues, or ensuring stalls do not run out of items in popular demand.

Another interesting use case could be related to compliance. Let's assume that alcohol sales are not allowed past the 4th quarter of a football game. Sales data from the POS terminals could be aggregated and the transactions validated against business rules. When an alcohol sale violation incident occurs, an alert is triggered and relevant information could be displayed on the stadium operations consoles and stadium maps, and passed onto an Incident Management system where a notification could be sent to other systems and devices, e.g. to alert staff with mobile devices. All data generated can be stored in a data warehousing system for subsequent analysis and reporting. 

The capabilities and use cases described in this article can be implemented on IBM's PureApplication System, a system designed to reduce IT complexity and accelerate time to value.  It  ships with all storage, networking and compute capabilities integrated into a rack in the factory, and pre-configured and optimized for Web Application and Database workloads. Patterns are used to capture best practice and accelerate the deployment and lifecycle management of pre-integrated, optimized industry solutions like the one described in this article.

Visit the IBM Smarter Stadium Solution for more information 

PureData for Analytics Value Proposition

The PureData for Analytics offering, a member of the PureSystems family of Expert Integrated Systems offers a simple-to-use approach for serious Analytics on structured data.

Analytics activities such as data exploration, discovery, transformation, model building and scoring can be performed where the data resides, in the warehouse.

This reduces the time it takes to build and deploy Analytics models, accelerating fact-based decision based on insightful Analytics.

Practitioners can also experiment iteratively with different models, operationalizing and making advanced Analytics more accessible.

What's a Field Programmable Gateway Array?

While conducting a deep dive into IBM's PureData for Analytics system, it became obvious that Field Programmable Gateway Array played a key part in its performance. Little did I know that this technology is used in many every day electronic devices, e.g. DVDs make use of it to facilitate reads of high quality compressed digital data off spinning discs without jitters.

The device itself is a semi conductor chip equipped with a large number of internal programmable gates. When programmed, it acts as a specialized hardware for specialized tasks requiring high performance.

In PureData for Analytics, data is delivered from disk to memory as quickly as it can be streamed off disk, compressed and cached in memory by the FPGA using a smart algorithm which ensure that frequently used data is served out of memory. The embedded engines in the FPGA can be dynamically modified and extended programmatically, and act on streaming data at extremely high speed. In addition to compressing the data using semiconductor based technology, the FPGA filters out unnecessary columns and rows to boost performance.

My first "in depth" look at the PureData for Analytics System

Hosts

The primary interface to the PureData for Analytics system are high performance Linux hosts. External tools and applications, e.g. reporting, backup and recovery etc, interact with the host via standardized interfaces, e.g. JDBC etc. The host compiles SQL queries into executable code snippets, creates optimized query plans and distributes the snippets to massive parallel processing nodes for execution. The host is in an active-passive high availability cluster configuration, mirroring data to the standby hosts which monitors the primary host and takes over in case of a failure.

S-blades

The bulk of the analytics workload processing occurs on intelligent massively parallel processing nodes called S-blades. S-blades are optimzed for processing analytics workloads at massive scale. They contain multi-core CPU, multiengine Field-Programmable Gate Architectures, and gigabytes of RAM, all optimized to work together to deliver peak performance. Continuous availability is made possible my the systems management software, which monitors the s-blades (including memory), and automatically takes a failed S-blade out of service and moves the processing load to a spare one.

Disks

The S-blades are connected to disk enclosures via a high-speed interconnect that enables streaming of data to the S-blade memory at the fastest rate possible. The disk enclosures contain high density, high performance disks. Redundancy is built into the data path from each S-blade to the disks. Each drive is mirrored in a RAID 1 configuration, and should a disk fail, the storage subsystem simply redirects I/O processing to the mirror without interruption of service. Spare drives are included, allowing the system to replace failed drives and regenerate content for full redundancy.

Network
The communication in the MPP grid occurs on an optimized IP based network designed for high volume data warehousing traffic patterns. It allows maximum utilization of the network bandwidth without overloading it, thereby allowing predictable performance close to the data transmission speed of the network. There are 2 completely independent networks for redundancy. The data network is also completely separate from the management network. This enables the system to assess the health of its components even where there might be data network problems.

Optimizing Database Warehousing Operations

The biggest challenge or bottleneck across date warehousing operations is the speed at which the database engine can read from and write to disk. This is commonly known as disk I/O bottleneck.

Most efficient analytics platforms minimize data movement, and are able to process streaming data from disk to memory in parallel, and on a massive scale. In the case of IBM's Netezza, this is accomplished by using innovative hardware acceleration. It uses Field Programmable Gate Arrays (FPGA) to filter extraneous data as early in the stream and as fast as the data can be streamed off disk.

By eliminating data that is not required close to the data source, downstream components like CPU, memory and network do not have to deal superfluous data, significantly reducing I/O bottlenecks and improving system performance.

Expert Integrated System Design

Expert Integrated Systems are a new breed of IT systems that ship pre integrated in the factory with processing, storage and networking components for faster time to value.

They are born optimized for a specific workload and built to deliver industry leading price-performance ratio with appliance simplicity.

The challenge for IT providers is to create a system that combines the best components, (rather than the most advanced or most highly performing), to create a design where the component s work together to deliver the best price -performance. In other words, an elegant design to overcome common IT operational challenges.

Key Elements of a Big Data Strategy

1) Visualisation and Discovery is an important capability. Organizations need to understand the scope and content of their data sources. Federated search, discovery and navigation tools enables access to information, irrespective of where it is located or format, restricting access to those with the authority to view the content. The ability to enrich the content, e.g. by adding comments, rating, tagging can be used to add a social dimension to how data is presented to the end user. The ability to quickly examine, explore and discover data relationships is can create a competitive advantage.

2) Hadoop enables organisations to reduce the cost of their data management infrastructure by offloading structured and unstructured data not suitable for traditional data warehouse for deep cost effective deep analytics.

3) What can you do when analyzing stored data is not good enough? Stream computing enables analysis of realtime streaming data of multiple formats; prefiltering (using complex calculations) and selective storing of high velocity data in realtime.

4) What can be done to accelerate the adoption of data warehousing and analytics capabilities that can offer a competitive advantage? As IBM has demonstrated with it's Netezza technology, there is a competitive advantage to be derived from purpose built systems for complex analytics workloads. These Expert Integrated Systems are designed with simplicity in mind, minimal administration required, and ability to perform complex analytics on large volumes of structured data at blistering speeds. Additionally, expertise can be built into this systems by packing pre-built analytics and visualizations applicable to specific industry applications. This library of pre-built functions, and tools that enable custom functions to be built, accelerate the time to value for analysis of data in native formats, where it lives.

5) Finally Security and Governance are key aspects of Big Data management. Sensitive data needs to be protected, retention policies need enforcing and data quality governed. Information lifecycle and master data management, along with data quality and governance services, are very important considerations in operating a Big Data platform.