Image Information Mining Conference: The Sentinels Era

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Published on March 8, 2014

Author: PierGiorgioMarchetti

Source: slideshare.net

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Quantifying the Value of Federated Datasets in Earth Observation Information Mining and Analytics

Volumes of EO data systematically collected, processed and stored is continuously increasing
It becomes more and more difficult evaluating their “value”
Datasets are made available by different institutions (agencies, commercial providers, etc.)  Federation of EO datasets
The Dataset Value is a vague concept: inherent information content, its possible exploitation, relation with user’s application needs, etc.

How to evaluate the value of an EO dataset in a typical scenario of a network of federated datasets?

Our work shows that the Representation Capacity of an EO product dataset D is proportional to the log of the cardinality of the EO dataset.
The value of a federation of datasets should take into account the Representation Capacity, and therefore grows with the log of the size of the individual datasets.
In order to evaluate and compare different datasets in a federation for further processing, a general methodology to preserve the relative information content has been defined.
New models for research and service support are emerging in the Earth Observation context / Data availability from forthcoming missions will increase rapidly
Facilities for EO dissemination and processing services, geographically distributed in a federated domain, largely scalable with reliable Quality of Services are urgently needed
Federated domains shall federate both computing and storage resources. The federation is valued and sustained by the underpinning Earth Observation datasets and their information content
To value datasets federations in wider contexts (e.g. Big Data, Web 2.0) R&D activities are needed to fully exploit the information they contain
A programmatic framework to sustain such R&D activities must be set-up to cover the various aspects involved (Image Information Mining, Time Series analysis, EO data analytics, multi-dimensional databases, semantic web, visual analytics, etc.)

Quantifying the Value of Federated Datasets in Earth Observation Information Mining and Analytics P.G. Marchetti (*), M. Iapaolo (**) * European Space Agency (ESA/ESRIN) EOP Research and Ground Segment Technology Section ** Randstad Italia c/o ESA/ESRIN Image Information Mining Conference 05/03/2014

Outline 1. Introduction 2. EO Datasets Value 3. Representation Capacity and Information Content for EO Datasets 4. Initial Results 5. Towards “Big Data” 6. Future work and perspectives Image Information Mining Conference 05/03/2014

Introduction 1. Volumes of EO data systematically collected, processed and stored is continuously increasing 2. It becomes more and more difficult evaluating their “value” 3. Datasets are made available by different institutions (agencies, commercial providers, etc.)  Federation of EO datasets 4. The Dataset Value is a vague concept: inherent information content, its possible exploitation, relation with user’s application needs, etc. How to evaluate the value of an EO dataset in a typical scenario of a network of federated datasets? Image Information Mining Conference 05/03/2014

EO Datasets Value Communication networks: the value of a network (its growth potential) grows as a quadratic function (n2) with the number of network nodes n (Metcalf’s Law) Generic concept of value (importance) applicable to a wide range of natural phenomena (occurrences of words in a text, size of population of big cities, etc.): the kth ranked item has a value (frequency, size) of about 1/k of the first one (Zipf’s Law) Total Value = sum of decreasing 1/k values over all the n items ≈ Applying to all n nodes: Image Information Mining Conference log(n) Total Value 05/03/2014 ≈ n log(n)

EO Datasets Value Plot of nlog(n) growing function, compared with the linear and quadratic one The origin is set on n=1. The Crossover Point with the Zipf’s law is obtained for larger n with respect to the Metcalf’s law Image Information Mining Conference 05/03/2014

EO Datasets Value and Information Content 1. In the EO context, it is of paramount importance to assess the value of datasets from the information content point of view (neither from growth potential nor from a market value ) 2. The actual exploitation of federated datasets is mainly based on their information content, extracted through time series analysis and image information mining techniques and analytics 3. The relative value (i.e. the information content) of an EO dataset permits to:  estimate the number of EO products (or samples) to be used  select which datasets are relevant for an analysis Need for a theoretical framework for the assessment of the value (information content) of a federation of EO datasets Image Information Mining Conference 05/03/2014

Representation Capacity Given a family of n non-overlapping datasets in a federation, D={D1,D2,…,Dn}; Select from D a sample S={S1, S2, …, Sn}, where each Sh is contained in Dh (h=1,2,…,n); Our aim is here to assess and quantify how much S is representative of D, and how it can characterise the value of D The Representation Capacity in D, K(D) is a measure for the degree of arbitrariness in choosing the sample S from D K(D) should be a non-decreasing function f(x) where x is the size of the set from which the images must be extracted Image Information Mining Conference 05/03/2014

Representation Capacity Image Information Mining Conference 05/03/2014

Information Content Image Information Mining Conference 05/03/2014

Representation Capacity 1. The Representation Capacity of an EO product dataset D is proportional to the log of the cardinality of the EO dataset 2. The value of a federation of datasets should take into account the Representation Capacity, and therefore grows with the log of the size of the individual datasets 3. In order to evaluate and compare different datasets in a federation for further processing, a general methodology to preserve the relative information content has been defined Image Information Mining Conference 05/03/2014

Comments 1. Additional constraints could be imposed by further processing, image mining, time series analysis and statistics/analytics objectives and requirements 2. The simplified approach presented in this paper could allow to assess the value (information content) a federation of EO dataset according to the Shannon’s theoretical framework 3. This approach should complement the one derived from the Zipf’s law, based on the number n of datasets in the federation, to help decision makers in evaluating the wealth of available information. Image Information Mining Conference 05/03/2014

Information Content Image Information Mining Conference 05/03/2014

Initial results 1-3 1. General approach for the assessment of the value – in terms of information content – of a federation of EO datasets 2. Interpretation of results under the Shannon information theoretical framework: o The information content of a dataset is proportional to its cardinality o Considering a sample of data extracted from the whole dataset, the Representation Capacity of the dataset is proportional to the log of its cardinality o As a consequence, the value (information content) of a federation of EO datasets grows with the log of the size of the individual datasets Image Information Mining Conference 05/03/2014

Initial results 2-3 Oops, if we have a look at the papers… Number of papers published on IEEE 3000 search performed on 14.02.2014 2500 2000 1500 Series1 1000 500 0 ESA Presentation | DD/MM/YYYY | Slide 14 ESA UNCLASSIFIED – For Official Use

Initial results 3-3 The identification of a general method for evaluating, comparing and selecting different datasets cannot ignore other information elements like: • the papers published and their quality, content, relevance, citations and impact factors e.g. (see Hirsch [1]) h-index • the papers published and related parameters: mission, sensor, area, … • the web pages published (see PageRank [2]) • Social media • … Image Information Mining Conference 05/03/2014

Future Work, towards “Big Data” 1. New models for research and service support are emerging in the Earth Observation context / Data availability from forthcoming missions will increase rapidly 2. Facilities for EO dissemination and processing services, geographically distributed in a federated domain, largely scalable with reliable Quality of Services are urgently needed 3. Federated domains shall federate both computing and storage resources. The federation is valued and sustained by the underpinning Earth Observation datasets and their information content 4. To value datasets federations in wider contexts (e.g. Big Data, Web 2.0) R&D activities are needed to fully exploit the information they contain 5. A programmatic framework to sustain such R&D activities must be setup to cover the various aspects involved (IIM, TS analysis, EO data analytics, multi-dimensional databases, semantic web, visual analytics, etc.) Image Information Mining Conference 05/03/2014

Future Work, towards “Big Data” 1. The programmatic framework should span a time frame of 5-10 years 2. It should include a strong user validation step (possibly involving hundreds of users and laboratories) 3. Should be extended to include other domains (not only EO!!): Earth and Space Science, Engineering … see the announced “Big Data from Space” Conference ! 4. Recent work (Mazzucato) demonstrates the benefits to fund large and strongly supported research programmes (venture capital and market will follow, exploiting former consistent investments by state funded institutions) 5. Research on value-enahnced search for EO data may help in adding value and is needed to exploit to the great variety of data which will be made available! Image Information Mining Conference 05/03/2014

References [1] J.E. Hirsch, An index to quantify an individual's scientific research output, Proceedings National Academy of Science 46:16569, 2005 [2] L. Page, S. Brin, R. Motwani, T. Winograd, The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab., 1999 ESA Presentation | DD/MM/YYYY | Slide 18 ESA UNCLASSIFIED – For Official Use

Thank you!! Image Information Mining Conference 05/03/2014

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