Log Log Laws for Empirical Measures

Analyzing Log Analysis: An Empirical Study of User Log Mining

We present an in-depth study of over 200K log analysis queries from Splunk, a platform for data analytics. Using these queries, we quantitatively describe log analysis behavior to inform the design of analysis tools. This study includes state machine based descriptions of typical log analysis pipelines, cluster analysis of the most common transformation types, and survey data about Splunk user ...

On Isoperimetric Inequalities for Log-convex Measures

Let μ = ρdx be a Borel measure on Rd. A Borel set A ⊂ R is a solution of the isoperimetric problem if for any B ⊂ R satisfying μ(A) = μ(B) one has μ(∂A) ≤ μ(∂B), where μ(∂A) = ∫ ∂A ρ dHd−1 is the corresponding surface measure. There exists only a small number of examples where the isoperimetric problem has an exact solution. The most important case is given by Lebesgue measure λ on R, the solut...

StUSPACE(log n) <= DSPACE(log²n / log log n)

We present a deterministic algorithm running in space O ? log 2 n= log logn solving the connectivity problem on strongly unambiguous graphs. In addition, we present an O(logn) time-bounded algorithm for this problem running on a parallel pointer machine.

Precise Rates in Log Laws for NA Sequences

Empirical log-optimal portfolio selections: a survey∗

In this paper the static portfolio and the dynamic log-optimal portfolio for memoryless and for stationary market processes are studied. We illustrate their performance (average annual yield) for NYSE data. 1 Notations Consider a market consisting of d assets. The evolution of the market in time is represented by a sequence of price vectors s1, s2, . . . ∈ R+, where sn = (s (1) n , . . . , s (d...