Modeling Dynamic Stock Correlation Graphs Sample Clauses

Modeling Dynamic Stock Correlation Graphs. ‌ V A D E To formally model such volatile graphs, we assume the existence of an infinite set of identifiers , attributes , atomic values , and inner-relationships . Each instance provides a value di for each attribute ai of the corresponding relation. The value di can be either atomic value, time information, or identifier, i.e., di∈D∪Z∪V. A relation R is a tuple with k attributes (a1, ..., ak), ai∈A. An instance r is a tuple (n, t, d1, ..., dk), where n∈V corresponds to the instance’s identifier, di∈D to the attribute values for the particular attribute ai, and t∈Z the time these values appeared for describing the time stock values correlations before 1 Oct g0 m0 y0 h0 i0 a0 none 1 Oct g1 (e.g. provider=“NasdaqGS”); y1 (e.g., stocks=200) ⊕(g, y); ⊕(g, m) 5 Oct y2; a1; h1; m1 ⊕(m, y); ⊕(g, h); ⊕(h, y); ⊗(g, m) 10 Oct ⊕f1; g2 15 Oct ⊕t1; a2; i1 ⊕(a, m); ⊕(a, f ); ⊗(m, y); ⊕(g, m); ⊕(t, y); ⊗(h, y) 20 Oct g3; f2 ⊗(g, h); ⊕(g, i); ⊕(f, m) 25 Oct m2; a3 ⊕(a, g); ⊗(g, m) 26 Oct ⊗f ⊗(f, ∗)
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