ΠΑΝΕΠΙΣΤΗΜΙΟ ΙΩΑΝΝΙΝΩΝ - ΤΜΗΜΑ ΠΛΗΡΟΦΟΡΙΚΗΣ SITE MAP | uoi ΕΛΛΗΝΙΚΑ | uoi ENGLISH
Algorithms for Big Data

(Undergraduate Course)

 

COURSE_ID:
MYE047
WEEKLY HOURS:
3, 0, 2
SEMESTER:
-
COURSE UNITS:
4
ECTS CREDITS:
5.00
COURSE HOMEPAGE:
-
YEAR 2018/2019:
YES
PREREQUISITE:
-
DESCRIPTION:

Selected topics from the following areas: Streaming: For a large dataset that is revealed as a stream, we shall see techniques for approximating its basic properties using a very small memory footprint. For example, stream sampling; estimation of statistical measurements (number of distinct elements, heavy hitters, frequency moments), or approximately solving graphs problems such as connectivity, shortest path distances, maximum matchings; stream filtering (bloom filters). Sketching: Construct a carefully designed set of measurements (i.e., a sketch) C(X) of a data set X, so as to we can learn the main parameters of X by only reading its sketch C(X). We will cover several results for p-stable sketches, sparse recovery and property testing, that answer this question for a range of fundamental problems. Graph stream algorithms: We shall discuss data sets representing graph structures, and elementary combinatorial problems on them, such as nearest neighbor, connectivity, distances, cut/spectral sparsifiers, spanners, matching, and graph sketching. Locality sensitive hashing: We shall study similarity estimation, and exploration of frequent itemsets in data sets, techniques for approximate nearest neighbour search, and data dependent hashing. Dimension reduction:The Johnson-Lindenstrauss lemma, principal component analysis, low-rank (e.g., SVD, CURE) approximation. Limited communication requirements: We shall discuss how to design algorithms for modern distributed computation models (e.g. MapReduce, Hadoop) that have low communication requirements. We will discuss graph sketching, a recently developed approach for designing low communication algorithms for processing dynamically changing graphs, as well as other techniques.

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