From R&D to industrialization and commercialization, deep tech today encompasses a broad ecosystem that includes multiple types of participants, each of which is active in one or more smaller ecosystems that is organized around a particular field of research, technology, industry, or mission. Business ecosystems are not new, of course, but deep tech ecosystems are nascent and operate in emerging--and therefore not yet stabilized--technologies and industries. As a consequence, they are a different breed and can be hard for traditional companies to navigate. Deep technologies also can affect entire value or supply chains and therefore require a more thorough analysis of the stakeholders' interdependencies and value creation models in order to determine how to align goals, set strategies, and organize for interaction with others. Newcomers can find themselves in unfamiliar territory, and carving out a role can be complex, but they need to find their way.
The massive investment of resources devoted to monitoring and assessment of economic and societal indicators in the United States is neither matched by nor linked to efforts to monitor and assess the ecosystem services and biodiversity that support economic and social well-being. Although national-scale assessments of biodiversity (1) and ecosystem indicators (2) have been undertaken, nearly a decade has elapsed since the last systematic assessment (2). A 2011 White House report called for a national biodiversity and ecosystem services assessment (3), but the initiative has stalled. Our aim here is to stimulate the process and outline a credible framework and pathway for an ongoing assessment of ecosystem functioning (see the photo). A national assessment should engage diverse stakeholders from multiple sectors of society and should focus on metrics and analyses of direct relevance to policy decisions, from local to national levels. Although many technical or science-focused components are in place, they need to be articulated, distilled, and organized to address policy issues.
Rather than attempting to hold ecosystems to an idealized conception of the past, as has been the prevailing conservation paradigm until recently, maintaining vibrant ecosystems for the future now requires new approaches that use both historical and novel conservation landscapes, enhance adaptive capacity for ecosystems and organisms, facilitate connectedness, and manage ecosystems for functional integrity rather than focusing entirely on particular species. Scientific breakthroughs needed to underpin such a paradigm shift are emerging at the intersection of ecology and paleobiology, revealing (i) which species and ecosystems will need human intervention to persist; (ii) how to foster population connectivity that anticipates rapidly changing climate and land use; (iii) functional attributes that characterize ecosystems through thousands to millions of years, irrespective of the species that are involved; and (iv) the range of compositional and functional variation that ecosystems have exhibited over their long histories. Such information is necessary for recognizing which current changes foretell transitions to less robust ecological states and which changes may signal benign ecosystem shifts that will cause no substantial loss of ecosystem function or services. Conservation success will also increasingly hinge on choosing among different, sometimes mutually exclusive approaches to best achieve three conceptually distinct goals: maximizing biodiversity, maximizing ecosystem services, and preserving wilderness. These goals vary in applicability depending on whether historical or novel ecosystems are the conservation target.
Something few people have grasped yet is that to get from Smart Technologies to IoT and on to Smart Living (Ubiquity) is a progression, not just in sensors, networks and device thinking, but also in ecosystem and task appraisal (to discern if they are even relevant not just the form of them) thinking. As with every other revolution not only does time and people change the meaning of the revolution they also change the trajectory. At the Global 5G Test Summit at MWC17 in Barcelona the panel was asked what services will 5G bring, extend or establish as the killer services, quite rightly the panel answered that the key services of 4G were not known until the network capability was in place and they evolved by adoption, not by strategy alone. Smart Technologies relates to limited networks of control actions, sensors and rules setting devices around a small number of tasks, specific locations or limited markets. They can be added to relatively easily but ultimately can't manage a whole ecosystem, without replacement.
Hadoop is the most popular Big Data framework, which can handle huge volumes of data. Hadoop comes with tons of ecosystem tools to solve different Big Data problems. Ecosystem played an important behind the popularity of Hadoop. With the ecosystem components, there are many solutions available for different problems, like unstructured data can be handled with MapReduce, structured data with Hive, machine learning algorithm with Mahout, text search with Lucene, data collection and aggregation using Flume, administration of cluster using Ambari and many more. Hadoop uses HDFS and MapReduce to process a large amount of data, and Hive for querying that data.