AT&T Labs Research — Leading Invention, Driving Innovation

The definitive version was published in Theory of Computing Systems Journal. , 2011-07-01

{We study scheduling algorithms for loading data feeds into real time data warehouses, which are used in applications such as IP network monitoring, online financial trading, and credit card fraud detection. In these applications, the warehouse collects a large number of streaming data feeds that are generated by external sources and arrive asynchronously. Data for each table in the warehouse are generated at a constant rate, different tables possibly at different rates. For each data feed, the arrival of new data triggers an emph{update} that seeks to append the new data to the corresponding table; if multiple updates are pending for the same table, they are batched together before being loaded. At time $ au$, if a table has been updated with information up to time $r le au$, its emph{staleness} is defined as $ au - r$. Our first objective is to schedule the updates on one or more processors in a way that minimizes the total staleness. In order to ensure fairness, our second objective is to limit the maximum ``stretch'', which we define (roughly) as the ratio between the duration of time an update waits till it is finished being processed, and the length of the update. In contrast to earlier work proving the nonexistence of constant-competitive algorithms for related scheduling problems, we prove that emph{any} online nonpreemptive algorithm, no processor of which is ever voluntarily idle, incurs a staleness at most a constant factor larger than an obvious lower bound on total staleness (provided that the processors are sufficiently fast). We give a constant-stretch algorithm, provided that the processors are sufficiently fast, for the emph{quasiperiodic model}, in which tables can be clustered into a few groups such that the update frequencies within each group vary by at most a constant factor. Finally, we show that our constant-stretch algorithm is also constant-competitive (subject to the same proviso on processor speed) in the quasiperiodic model with respect to total weighted staleness, where tables are assigned weights that reflect their priorities.}

The definitive version was published in ACM-SIAM Symposium on Discrete Algorithms (SODA) , 2011-01-23

{We introduce Capacitated Metric Labeling. As in Metric Labeling, we are given a weighted graph G = (V, E), a label set L, a semimetric d_L on this label set, and an assignment cost function phi: V x L -> R, and the goal, as in Metric Labeling, is to find an assignment f: V->L that minimizes a particular two-cost function, but in Capacitated Metric Labeling with the additional restriction that each label t_i receive at most l_i nodes. We give a O(log |V|)-approximation algorithm when the number of labels is fixed. We prove that it is impossible to approximate the value of an instance of Capacitated Metric Labeling to within any finite factor, if P!=NP. Furthermore, we prove that any finite-ratio polynomial-time approximation algorithm must multiplicatively violate the label capacities by Omega((log |L|)^(1/2-eps)). }

{We present Data Auditor, a tool for exploring data quality and semantics. Data Auditor provides a variety of semantic rule (constraint) classes for asserting consistency and completeness properties of the data. It can be used to test hypotheses by instantiating rules over supplied sets of attributes, to see if such rules hold or fail (to a required degree) on a minimum fraction of the data. Regions of the data for which the rule is satisfied (alternatively, violated) are concisely and meaningfully summarized in a tableau. }

The definitive version was published in European Symposium on Algorithms , 2009-09-07

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Awards
ACM Fellow, 2011. For contributions to the design and analysis of algorithms