Mining of Massive Datasets

By Anand Rajaraman, Jeffrey D. Ullman. 2011

1,021 citations recorded by the source corpus.

The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

Subfields: Information Systems, ML Systems & Hardware

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Citations in the corpus

Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) (2014, methodology): “makes p 1 and p 2 closer. Smaller m pushes p 1 and p 2 towards left and thereby increasing the gap between them. 11 Small ρ = logp1 logp2 roughly boils down to having a bigger gap between p 1 and p 2 [26]. Ideally, for small ρ, we would like to use big U and small m. The error ǫ = U2m+1 exactly foll”
Autoregressive Entity Retrieval (2020, methodology): “ct to the best performing baseline. In Table 3 we compare GENRE against all methods reported in the public leaderboard: DPR (Karpukhin et al., 2020), DPR+BERT (Devlin et al., 2019), DPR+BART, tf-idf (Leskovec et al., 2014), RAG (Lewis et al., 2020), and BLINK+ﬂair (Wu et al., 2019; Akbik et al., 201”
CARER: Contextualized Affect Representations for Emotion Recognition (2018, methodology): “Before using the patterns for classification, they need to be weighted using a weighting mechanism such as tf-idf (Leskovec et al., 2014).”
Natural Language Processing Advancements By Deep Learning: A Survey (2020, background): “plest example can be document categorization where features are words and weights are TF-IDF scores [47] of the associated words.”
BioMedBERT: A Pre-trained Biomedical Language Model for QA and IR (2020, methodology): “To debias the dataset, we remove the records that have a very high percentage of common thresholds (based on the Jaccard index) (Leskovec et al., 2020).”
Theory and applications of b-bit minwise hashing (2011, methodology): “Our technique yields a very simple algorithm and can be realized with only minor modifications to the original minwise hashing scheme.”
Portable Automatic Text Classification for Adverse Drug Reaction Detection via Multi-corpus Training (2014, background): “Idf values [45] for the semantic types and CUIs.”
The Matthew effect in empirical data (2014, background): “Indeed, ‘big data’ [159] is the keyword for current complex systems research, and the data windfall is also surely going to promote research on the Matthew effect.”
Sequence Synopsis: Optimize Visual Summary of Temporal Event Data (2018, methodology): “To tackle this challenge, we propose a fast randomized approximation algorithm utilizing Locality Sensitive Hashing (LSH) [22].”
CSI: A Hybrid Deep Model for Fake News Detection (2017, methodology): “gaged with, and apply the Singular Value Decomposition (SVD) to extract a lower-dimensional representation for eachui; this technique is commonly used in information retrieval and recommender systems [17]. Finally, a vector xτ is included which carries the text characteristic of an engagement with a”
Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters (2020, methodology): “Our second approach is implemeted using BFR algorithm [19] to further merge the closest clusters together.”
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage (2016, background): “Within the remaining space of choices, we note that a very similar problem of downweighting based on column frequency appears in deciding the form of the ‘inverse document frequency’ term of the popular heuristic tf-idf weighting scheme [24], in which logarithmic weighting of frequency has been empi”