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Lukasz Golab

from Morris Plains, NJ
Age ~45
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Also known as:
Anneliese Zausner-Mannes
Phone and address:
1 Laurel St, Greystone Park, NJ 07950
Related to:
Sara ZausnerTavish Zausner MannesTavish L ZausnerEdwin E Gola, 84W E GolaSuzanne M Derosa, 49Mariola Golab, 47Michele A Heaney, 54Suzanne Gola
Connected to:
Bogdan Golab, 42Coleen M Golab, 53Tami Golab, 38Tawana L Golab, 51Ali A Golabchi, 61Daniel Golabek, 28Jennifer A Golabek, 50Slawomir Golabek, 47Ruth A Golabiewski, 48Beverly D Golaboski, 70

Lukasz Golab Phones & Addresses

  • 1 Laurel St, Morris Plains, NJ 07950
  • South Amboy, NJ
  • 25 Hickory Pl, Chatham, NJ 07928

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Higher Education

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Lukasz Golab Photo 1

Assistant Professor At University Of Waterloo

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Location:
Waterloo, Ontario, Canada
Industry:
Higher Education

Publications

Us Patents

Optimized Field Unpacking For A Data Stream Management System

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US Patent:
8112415, Feb 7, 2012
Filed:
Jun 4, 2008
Appl. No.:
12/132690
Inventors:
Theodore Johnson - New York NY, US
Lukasz Golab - Morris Plains NJ, US
Oliver Spatscheck - Randolph NJ, US
Assignee:
AT&T Intellectual Property I, LP - Atlanta GA
International Classification:
G06F 17/30
G06F 7/00
US Classification:
707718, 707719, 707721, 707758
Abstract:
Two methods and computer-readable medium for obtaining information using field group unpacking functions. The first method obtains information using field group unpacking functions by identifying an optimized unpacking function from field group unpacking functions, and an optimized unpacking function is used to unpack a field associated with the data stream. The second method obtains information using field group unpacking functions by identifying an optimized unpacking function from the field group unpacking functions. Then, a prefilter is applied and associated with the optimized unpacking functions and used to unpack a field associated with the data stream. The computer-readable medium obtains field group unpacking functions for execution by a computing device using field group unpacking functions that identify an optimized unpacking function from the field group unpacking functions, and use an optimized unpacking function to unpack a field associated with the data stream.

Method For Scheduling Updates In A Streaming Data Warehouse

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US Patent:
8453155, May 28, 2013
Filed:
Nov 19, 2010
Appl. No.:
12/950730
Inventors:
Lukasz Golab - Morris Plains NJ, US
Theodore Johnson - New York NY, US
Vladislav Shkapenyuk - New York NY, US
Assignee:
AT&T Intellectual Property I, L.P. - Atlanta GA
International Classification:
G06F 9/46
US Classification:
718104, 718102
Abstract:
A method for scheduling atomic update jobs to a streaming data warehouse includes allocating execution tracks for executing the update jobs. The tracks may be assigned a portion of available processor utilization and memory. A database table may be associated with a given track. An update job directed to the database table may be dispatched to the given track for the database table, when the track is available. When the track is not available, the update job may be executed on a different track. Furthermore, pending update jobs directed to common database tables may be combined and separated in certain transient conditions.

Generating Conditional Functional Dependencies

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US Patent:
8639667, Jan 28, 2014
Filed:
Mar 3, 2009
Appl. No.:
12/380858
Inventors:
Lukasz Golab - Morris Plains NJ, US
Howard Karloff - New York NY, US
Philip Korn - New York NY, US
Divesh Srivastava - Summit NJ, US
Bei Yu - Somerville MA, US
Assignee:
AT&T Intellectual Property I, L.P. - Reno NV
International Classification:
G06F 7/00
G06F 17/00
G06F 17/30
US Classification:
707690, 707696, 707741, 707792
Abstract:
Techniques are disclosed for generating conditional functional dependency (CFD) pattern tableaux having the desirable properties of support, confidence and parsimony. These techniques include both a greedy algorithm for generating a tableau and, for large data sets, an “on-demand” algorithm that outperforms the basic greedy algorithm in running time by an order of magnitude. In addition, a range tableau, as a generalization of a pattern tableau, can achieve even more parsimony.

Processing Data Using Sequential Dependencies

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US Patent:
8645309, Feb 4, 2014
Filed:
Nov 30, 2009
Appl. No.:
12/592586
Inventors:
Lukasz Golab - Morris Plains NJ, US
Howard Karloff - New York NY, US
Philip Korn - New York NY, US
Divesh Srivastava - Summit NJ, US
Avishek Saha - West Bengal, IN
Assignee:
AT&T Intellectual Property I. L.P. - Atlanta GA
International Classification:
G06F 17/00
G06N 7/04
G06N 5/02
G06N 7/00
G06N 7/08
G06F 11/00
G06F 11/30
G06C 25/00
H03M 13/00
H04L 1/00
US Classification:
706 54, 706 47, 706 48, 706 59, 714746
Abstract:
The specification describes data processes for analyzing large data steams for target anomalies. “Sequential dependencies” (SDs) are chosen for ordered data and present a framework for discovering which subsets of the data obey a given sequential dependency. Given an interval G, an SD on attributes X and Y, written as X→G Y, denotes that the distance between the Y-values of any two consecutive records, when sorted on X, are within G. SDs may be extended to Conditional Sequential Dependencies (CSDs), consisting of an underlying SD plus a representation of the subsets of the data that satisfy the SD. The conditional approximate sequential dependencies may be expressed as pattern tableaux, i. e. , compact representations of the subsets of the data that satisfy the underlying dependency.

Efficient Predicate Prefilter For High Speed Data Analysis

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US Patent:
20090171890, Jul 2, 2009
Filed:
Jan 2, 2008
Appl. No.:
12/006300
Inventors:
Theodore Johnson - New York NY, US
Lukasz Golab - Morris Plains NJ, US
Oliver Spatscheck - Randolph NJ, US
Assignee:
AT&T Labs, Inc. - Austin TX
International Classification:
G06F 17/30
US Classification:
707 2, 707E17131
Abstract:
A method and system are disclosed for operating a high speed data stream management system which runs a query plan including a set of queries on a data feed in the form of a stream of tuples. A predicate prefilter is placed outside the query plan upstream of the set of queries, and includes predicates selected from those used by the queries. Predicates are selected for inclusion in the prefilter based on a cost heuristic, and predicates are combined into composites using a rectangle mapping heuristic. The prefilter evaluates the presence of individual and composite predicates in the tuples and returns a bit vector for each tuple with bits representing the presence or absence of predicates in the tuple. A bit signature is assigned to each query to represent the predicates related to that query, and a query is invoked when the tuple bit vector and the query bit signature are compatible.

Minimizing Staleness In Real-Time Data Warehouses

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US Patent:
20110040727, Feb 17, 2011
Filed:
Aug 11, 2009
Appl. No.:
12/539429
Inventors:
Lukasz Golab - Morris Plains NJ, US
Mohammad Hossein Bateni - Princeton NJ, US
Mohammad Hajiaghayi - Florham Park NJ, US
Howard Karloff - New York NY, US
Assignee:
AT&T INTELLECTUAL PROPERTY I, L.P. - Reno NV
International Classification:
G06F 17/30
G06F 9/46
US Classification:
707618, 718102, 707E17005
Abstract:
Data tables in data warehouses are updated to minimize staleness and stretch of the data tables. New data is received from external sources and, in response, update requests are generated. Accumulated update requests may be batched. Data tables may be weighted to affect the order in which update requests are serviced.

Conservation Dependencies

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US Patent:
20120130935, May 24, 2012
Filed:
Nov 23, 2010
Appl. No.:
12/927770
Inventors:
Lukasz Golab - Morris Plains NJ, US
Howard Karloff - New York NY, US
Philip Korn - New York NY, US
Divesh Srivastava - Summit NJ, US
Barna Saha - Hyattsville MD, US
International Classification:
G06N 5/02
US Classification:
706 52, 706 46
Abstract:
Given a set of data for which a conservation law is an appropriate characterization, “hold” and/or “fail” tableaux are provided for the underlying conservation law, thereby providing a conservation dependency whereby portions of the data for which the law approximately holds or fails can be discovered and summarized in a semantically meaningful way.

Methods, Systems, And Products For Maintaining Data Consistency In A Stream Warehouse

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US Patent:
20120209817, Aug 16, 2012
Filed:
Feb 15, 2011
Appl. No.:
13/027486
Inventors:
Lukasz Golab - Morris Plains NJ, US
Theodore Johnson - New York NY, US
Assignee:
AT&T INTELLECTUAL PROPERTY I, L.P. - Atlanta GA
International Classification:
G06F 17/30
US Classification:
707688, 707E17005
Abstract:
Methods, systems, and products characterize consistency of data in a stream warehouse. A warehouse table is derived from a continuously received a stream of data. The warehouse table is stored in memory as a plurality of temporal partitions, with each temporal partition storing data within a contiguous range of time. A level of consistency is assigned to each temporal partition in the warehouse table.
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Lukasz Golab from Morris Plains, NJ, age ~45 Get Report