Called IBM PAIRS (Physical Analytics Integrated Data Repository & Services) Geoscope, the service is aimed at "accelerating the discovery of new insights" by extracting value from geospatial-temporal big data - so named because of its inherent link to place and time. Such data, which is vast in scope and growing exponentially, is being fueled by increasingly sophisticated and affordable electronics in applications such as the Internet of Things (IoT).
Extracting insights from geospatial-temporal big data - which includes satellite and aerial imagery, global-scale data and models (weather, climate, oceans, etc.), geo-referenced IoT/sensor networks, and big-event data captured on platforms like Twitter and GDELT - poses a significant challenge. While often freely available, its massive size and the complexities associated with its preparation for use make it difficult to exploit and scale, says the company, especially for large areas and time-critical applications.
For example, geospatial-temporal datasets are often too large to transfer for analysis in a reasonable time. And according to some projections, data generation rates from just IoT alone could reach 600 zettabytes (ZB) per year by 2020, .
Another challenge hindering rapid analysis is the "daunting array" of complex formats of various geospatial-temporal datasets. Understanding and curating this diversity can present significant and sometimes insurmountable bottlenecks when attempting to bring the data to the analytics.
"PAIRS Geoscope addresses this problem by reversing the situation - that is, by offering a service that allows clients to bring their analytics to the data." says Hendrik Hamann, Senior Manager, Physical Analytics, IBM Research. "It frees clients from the cumbersome processes that dominate conventional geospatial-temporal data acquisition and preparation and provides search-friendly, ready access to a rich, diverse, and growing catalog of historical and continuously updated geospatiotemporal information."
PAIRS Geoscope is built on a highly scalable, cloud-based repository - currently growing by terabytes per day - especially crafted for the complexities of geospatial-temporal information, says the company. The repository can automatically ingest, curate,