Zero prediction bias an appealing concept in connectivity constraint computing
April 03, 2017
The leading names in the global ICT industry have bagged top honors in the global connectivity constraint computing market as well. For 2015, the glob...
The leading names in the global ICT industry have bagged top honors in the global connectivity constraint computing market as well. For 2015, the global connectivity constraint computing market was dominated by the names Microsoft Corporation, Google, Inc., Amazon.com, Inc., and Wal-Mart Stores, Inc.
As per a recently published research report, the global connectivity constraint computing market holds a higher concentration of players in North America than other regions. Asia Pacific, which was the smaller market in 2015, is expected to show a meteoric growth rate due to its booming IT industry and increasing scope of application for connectivity constraint computing due to high rate of urbanization. The demand for connectivity constraint computing is also expected to be high in regions that are planning for ecosystem management and wildlife corridor planning. The MEA region is likely to gain momentum in infrastructural development, spurring the demand for CCC modeling. The global connectivity constraint computing market is expected to expand at a meteoric CAGR of 58.4 percent within a forecast period from 2016 to 2024. While its expected evaluation for the end of 2016 is expected to be $177.8 million USD, the phenomenal growth rate is expected to have the market reach $7.03 billion USD by the end of 2024.
The use of artificial computing introduces the highly sought after concept of error elimination and bias protection for computing systems. Human operators, regardless of skill, are still prone to a certain minimal level of errors, especially in the massive data sets that are being crunched in modern times. The dynamic nature of data structures calls for the use of connectivity constraint computing and other artificial computing methods, that essentially, minimize or even negate the scope of error, as well as the overall time taken for the processes, through fast and iterative computing. This is becoming a highly crucial need for end users when faced with the staggering data volumes that need to be processed without any bias, which thus forms the key driver for the global connectivity constraint computing market.
Other factors promoting the growth of the global connectivity constraint computing market include the growing need for process automation in terms of improvement of quality in data accumulation and maintenance functions, and the growing need for CCC for spatial analysis using data rich and large scale frameworks.
One of the key restrictions on the global connectivity constraint computing market currently, is the increasing complexity of computing due to the formation of high volume and multiple data sets. The CCC modeling techniques, while useful in solving several issues, itself required the use of multiple data sets. This can lead to the accumulation of data sets that are non-compatible and can seriously hinder the automation process. At the same time, the global connectivity constraint computing market is also hindered by the expensive nature of model implementation, as real time data crunching and CCC modeling is certainly a costly affair at the moment.
While these issues are likely to be resolved over the coming years, players in the global connectivity constraint computing market can also look forward to gaining several opportunities in terms of constraint inclusions. The inclusion of proper constraint programming approaches can help resolve the issues visible in the use of this modeling in connected wildlife corridors. Additionally, the global connectivity constraint computing market is also expected to benefit vastly from the increasing scope of application of CCC in urban planning.