* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Discovering spatio-temporal cascade patterns
2009 United Nations Climate Change Conference wikipedia , lookup
Myron Ebell wikipedia , lookup
German Climate Action Plan 2050 wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Global warming hiatus wikipedia , lookup
ExxonMobil climate change controversy wikipedia , lookup
Global warming wikipedia , lookup
Global warming controversy wikipedia , lookup
Climate resilience wikipedia , lookup
Soon and Baliunas controversy wikipedia , lookup
Economics of global warming wikipedia , lookup
Instrumental temperature record wikipedia , lookup
Climate change adaptation wikipedia , lookup
Heaven and Earth (book) wikipedia , lookup
General circulation model wikipedia , lookup
Climate change feedback wikipedia , lookup
Climate sensitivity wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Fred Singer wikipedia , lookup
Politics of global warming wikipedia , lookup
Climate change denial wikipedia , lookup
Climatic Research Unit email controversy wikipedia , lookup
Carbon Pollution Reduction Scheme wikipedia , lookup
Michael E. Mann wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Climate engineering wikipedia , lookup
Climate governance wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Climate change in the United States wikipedia , lookup
Solar radiation management wikipedia , lookup
Global Energy and Water Cycle Experiment wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Climatic Research Unit documents wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Climate change and poverty wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
IPCC Fourth Assessment Report wikipedia , lookup
Climate change, industry and society wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Discovering spatio-temporal cascade patterns Given a collection of Boolean spatio-temporal(ST) event types, the spatio-temporal cascade pattern (STCP) discovery process finds partially ordered subsets of event-types whose instances are located together and occur in stages[1]. For example, analysis of climate science datasets may reveal frequent occurrence of glacier melting, intense flooding with rainfall in some areas and drought in other areas after global warming. Figure 1, shows a plausible network of interactions among spatio-temporal events in climate science datasets [2]. ST cascade patterns in this context may discover frequent sub-networks such as increase green house gases may often be followed by global warming, early snow/glacier melting as well as local droughts cascading to shortage in food supply in some areas. STCP analysis from ST datasets is also important for application domains such as public safety (e.g. crime attractors and generators) and natural disaster planning(e.g. hurricanes). Figure 1: Cascading spatio-temporal patterns from climate science datasets[2] However, discovering STCP from large ST climate science data is challenging for several reasons, including both the lack of computationally efficient, statistically meaningful metrics to quantify interestingness, and the large cardinality of candidate pattern sets that are exponential in the number of event types. Existing literature in ST data mining focuses on mining totally ordered sequences or unordered subsets. Traditional methods in graph mining and machine learning (e.g. Bayesian network family) either deal with computationally expensive interest measures (e.g. Maximum independent set or represent joint probability distributions), which may take exponential computations even for individual candidate patterns. In contrast, we propose to new interest measures which are computationally less expensive but still provide spatial statistical interpretation in terms of Ripley’s K-function [3] generalized for spatio-temporal events. Preliminary approach [1] addressed cascade patterns in context of single-jurisdiction crime analysis summarizing local interactions among nearby events, such as bar closing followed by unruly behavior or drunk driving in the neighborhood. However, climate datasets and event interactions violate many assumptions underlying the preliminary work [1]. For example, interactions among climate events may involve long distances or tele-connection. El-nino may impact precipitation and temperature thousands of miles away in a non-linear manner[4]. In addition, global climate datasets are continuous over space and time. The overall goal of the proposed work is to address the new challenges in cascade pattern mining for understanding climate change. The proposed work is structured as a set of tasks described next. Research Tasks We propose the following research tasks for STCP analysis in the context of climate science: RT1: Explore novel ST data models (e.g. data-types, operations) to represent new properties (e.g. non-linear long-distance interaction) of climate science datasets. RT2: Explore novel ST measures to quantify interesting and useful cascade patterns in climate datasets. A key challenge is to balance conflicting requirement of computational cost and need for scientific interpretation. RT3: Explore novel and computationally efficient algorithms to that can scale to large climate science datasets. RT4: Evaluate proposed interest measures by using spatial statistical metrics like ST K-Function, Knox Index, Jacquez Index etc.[1,3]. References [1] Pradeep Mohan ,Shashi Shekhar, James A.Shine, James P.Rogers. Cascading Spatio-temporal pattern discovery: A summary of results, In Proc. of 10th SIAM Int’ l Conf. on Data Mining (SDM 2010), Columbus, OH, 2010. [2] Committee on Strategic Advice on the U.S. Climate Change Science Program; National Research Council: Restructuring Federal Climate Research to Meet the Challenges of Climate Change. The National Academies Press, Washington D.C., 2009. [3] Martin Kulldorff, Ulf Hjalmars. The Knox Method and Other Tests for Space-time Interaction, Biometrics, 55, 544-552, June 1999. [4] M. Hoerling, A. Kumar, M. Zhong, El Niño, La Niña, and the Nonlinearity of Their Teleconnections, Journal of Climate, 10(8), American Meteorological Society, August 1997.