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Spatio-Temporal Data Awareness in ( the Evolution of ) Food , Water and Energy Resources NSF Workshop on Identify Interdisciplinary Data Science Approaches and Challenges to Enhance Understanding of Interactions of Food Systems with Energy and Water Systems

This position paper addresses a specific context of the problem of improving the yield of agricultural resources, and proposes a general paradigm that may be used to improve the use/management of other resources. The problems identified and tackled come from the two complementary perspectives: (1) How to capitalize on the wealth of data; and (2) How to use that data as a “feedback mechanism” to enable efficient and effective planning/actuation in various spatio-temporal scales. Introduction and Motivation The United Nations Food and Agriculture Organization has data-repositories since 1945, e.g., statistical data related to crops, livestock, irrigation, land use, fertilizer, pesticide consumption, along with a global spatial database of land-use (cf. http://www.fao.org/corp/statistics/en/ ). However, that data, along with other similar repositories, is heterogeneous – i.e., the type and the storage-structures vary, in addition to the variable levels of granularities used for aggregating it. The water-resources data of the Food and Agriculture Organization (AQUASTAT) summarized by continents and major region is an Excel file; the Food (FASTAT) data is maintained by a different software. Similarly, the USGS produced National land cover data (NLCD) uses a 21-class classification scheme, whereas USDA generated crop data layer (CDL) has more than 100 classes. The remote-sensing data used for ET-management, varies not only in its resolution [1,2], but also in its quality, based on different atmospheric conditions -e.g., clouds occluding the satellites [3]. Validating different agricultural planning-models [4] for predicting crop growth, fertility and insecticiede/fungicides protection and the responses of those models to spatial variability [5] require more sophisticated mining and warehousing techniques. The main postulate of this position paper is that, while there are many existing projects tackling certain aspects of applying data-driven methodologies in the domains of precision agriculture with environmental context-awareness – they have not explored the potential of coupling(s) between the model-based approaches and the data-driven approaches: (1) various association rules across the heterogeneous data, e.g.: data from sensing devices measuring canopy temperatures, leaf chlorophyll, soil moisture and meteorological variables such as solar radiation, wind, relative humidity [6] at different scales and formats using ground sensors and satellite images [7]. Complementary to this, the existing research on warehousing and mining spatio-temporal data [8, 9], has not been integrated with identification and classification of the impact that various geo-physical processes and events can have on agricultural practices [10]. Figure 1 illustrates the different feedback control Fig. 1 Agriculture – coupling of data and control loops at various spatio-temporal resolutions yield a hierarchy of controller-data interactions in 3 levels. At the lowest levels, actuators respond automatically to readings from nearby sensors in an effort to keep local performance variables at desired levels – e.g., the average moisture of the soil over a local region. The intermediate levels implement a novel data-in-theloop version of a model predictive control (MPC) strategy. The objective at these levels is to make optimal control scheme more robust by incorporating data-feedback (and notifications) from both in-field sensors, as well as short-term weather prediction systems. Speeding up the computation of the control-trajectories and determining control-horizons will require efficient evaluation of spatio-temporal predicates over both recent-historic data and streams of current data, along with trade-offs between the uncertainty and the turnaround time(s). The highest levels of the hierarchy illustrates a data-driven supervisory control, based on heterogeneous historic data from different sources, and executing computationally demanding tasks of progressively narrowing the selection of the optimal model to be used in a given context. DNAAC: Data-Natural Association of Analytics and Control To achieve the desiderata outlined in the previous section, we postulate that a paradigm shift is needed, that will enable relevant ties to be established between the data-values and the actuation modules that can regulate the behavior of the sources of those data-values. Visually, the DNAAC can be represented in a manner resembling the double-helix of the human DNA, as shown in Figure 2. This model postulates that for every activity in the control/ analytics module, there may exist some data-management activity that could improve certain important aspects – spanning from computational efficiency, to improving the overall Quality of Service guarantees under temporal constraints (and uncertainty). Clearly, different applications may exhibit different contextdependent parameters – however, at the heart is the same quest of improving a particular “output parameter” based on a collection of inputs, off of which same are controllable and Fig. 2 Coupling of Data/Analytics and Control some are not. Specifically, let us consider the two classical (and desirable properties): 1.   Controllability: the capability to traverse the entire configuration space of a given system – i.e., the ability of an external input to move the internal state of a system from any initial state to any other final state. 2.   Observability: a mathematical-dual of controllability – a measure for how well internal states of a system can be inferred by knowledge of its external outputs. The important note is that validation of each of them requires execution of algorithms which involve inversion of large matrices. What Figure 2 is attempting to illustrate is: is there any “easier” way, via traditional query processing? We are inclined to believe so. But one example is our work [55] where we demonstrated that in the settings of tracking in Wireless Sensor Networks, a simple spatio-temporal location query under a dead-reckoning policy provides a comparable accuracy of the tracking, while saving a substantial amount of energy by avoiding the computation-heavy calculations involving distributed particle filters.