Local Access to Huge Random Objects Through Partial Sampling

© Amartya Shankha Biswas, Ronitt Rubinfeld, and Anak Yodpinyanee. Consider an algorithm performing a computation on a huge random object (for example a random graph or a “long” random walk). Is it necessary to generate the entire object prior to the computation, or is it possible to provide query access to the object and sample it incrementally “on-the-fly” (as requested by the algorithm)? Such an implementation should emulate the random object by answering queries in a manner consistent with an instance of the random object sampled from the true distribution (or close to it). This paradigm is useful when the algorithm is sub-linear and thus, sampling the entire object up front would ruin its efficiency. Our first set of results focus on undirected graphs with independent edge probabilities, i.e. each edge is chosen as an independent Bernoulli random variable. We provide a general implementation for this model under certain assumptions. Then, we use this to obtain the first efficient local implementations for the Erdös-Rényi G(n, p) model for all values of p, and the Stochastic Block model. As in previous local-access implementations for random graphs, we support Vertex-Pair and Next-Neighbor queries. In addition, we introduce a new Random-Neighbor query. Next, we give the first local-access implementation for All-Neighbors queries in the (sparse and directed) Kleinberg’s Small-World model. Our implementations require no pre-processing time, and answer each query using O(poly(log n)) time, random bits, and additional space. Next, we show how to implement random Catalan objects, specifically focusing on Dyck paths (balanced random walks on the integer line that are always non-negative). Here, we support Height queries to find the location of the walk, and First-Return queries to find the time when the walk returns to a specified location. This in turn can be used to implement Next-Neighbor queries on random rooted ordered trees, and Matching-Bracket queries on random well bracketed expressions (the Dyck language). Finally, we introduce two features to define a new model that: (1) allows multiple independent (and even simultaneous) instantiations of the same implementation, to be consistent with each other without the need for communication, (2) allows us to generate a richer class of random objects that do not have a succinct description. Specifically, we study uniformly random valid q-colorings of an input graph G with maximum degree ∆. This is in contrast to prior work in the area, where the relevant random objects are defined as a distribution with O(1) parameters (for example, n and p in the G(n, p) model). The distribution over valid colorings is instead specified via a “huge” input (the underlying graph G), that is far too large to be read by a sub-linear time algorithm. Instead, our implementation accesses G through local neighborhood probes, and is able to answer queries to the color of any given vertex in sub-linear time for q ≥ 9∆, in a manner that is consistent with a specific random valid coloring of G. Furthermore, the implementation is memory-less, and can maintain consistency with non-communicating copies of itself

Neo: A Learned Query Optimizer

Query optimization is one of the most challenging problems in database systems. Despite the progress made over the past decades, query optimizers remain extremely complex components that require a great deal of hand-tuning for specific workloads and datasets. Motivated by this shortcoming and inspired by recent advances in applying machine learning to data management challenges, we introduce Neo (Neural Optimizer), a novel learning-based query optimizer that relies on deep neural networks to generate query executions plans. Neo bootstraps its query optimization model from existing optimizers and continues to learn from incoming queries, building upon its successes and learning from its failures. Furthermore, Neo naturally adapts to underlying data patterns and is robust to estimation errors. Experimental results demonstrate that Neo, even when bootstrapped from a simple optimizer like PostgreSQL, can learn a model that offers similar performance to state-of-the-art commercial optimizers, and in some cases even surpass them

Revisiting the Economics of Privacy: Population Statistics and Confidentiality Protection as Public Goods

This paper has been replaced with http://digitalcommons.ilr.cornell.edu/ldi/37. We consider the problem of the public release of statistical information about a population–explicitly accounting for the public-good properties of both data accuracy and privacy loss. We first consider the implications of adding the public-good component to recently published models of private data publication under differential privacy guarantees using a Vickery-Clark-Groves mechanism and a Lindahl mechanism. We show that data quality will be inefficiently under-supplied. Next, we develop a standard social planner’s problem using the technology set implied by (ε, δ)-differential privacy with (α, β)-accuracy for the Private Multiplicative Weights query release mechanism to study the properties of optimal provision of data accuracy and privacy loss when both are public goods. Using the production possibilities frontier implied by this technology, explicitly parameterized interdependent preferences, and the social welfare function, we display properties of the solution to the social planner’s problem. Our results directly quantify the optimal choice of data accuracy and privacy loss as functions of the technology and preference parameters. Some of these properties can be quantified using population statistics on marginal preferences and correlations between income, data accuracy preferences, and privacy loss preferences that are available from survey data. Our results show that government data custodians should publish more accurate statistics with weaker privacy guarantees than would occur with purely private data publishing. Our statistical results using the General Social Survey and the Cornell National Social Survey indicate that the welfare losses from under-providing data accuracy while over-providing privacy protection can be substantial

Ranking for Scalable Information Extraction

Information extraction systems are complex software tools that discover structured information in natural language text. For instance, an information extraction system trained to extract tuples for an Occurs-in(Natural Disaster, Location) relation may extract the tuple from the sentence: "A tsunami swept the coast of Hawaii." Having information in structured form enables more sophisticated querying and data mining than what is possible over the natural language text. Unfortunately, information extraction is a time-consuming task. For example, a state-of-the-art information extraction system to extract Occurs-in tuples may take up to two hours to process only 1,000 text documents. Since document collections routinely contain millions of documents or more, improving the efficiency and scalability of the information extraction process over these collections is critical. As a significant step towards this goal, this dissertation presents approaches for (i) enabling the deployment of efficient information extraction systems and (ii) scaling the information extraction process to large volumes of text. To enable the deployment of efficient information extraction systems, we have developed two crucial building blocks for this task. As a first contribution, we have created REEL, a toolkit to easily implement, evaluate, and deploy full-fledged relation extraction systems. REEL, in contrast to existing toolkits, effectively modularizes the key components involved in relation extraction systems and can integrate other long-established text processing and machine learning toolkits. To define a relation extraction system for a new relation and text collection, users only need to specify the desired configuration, which makes REEL a powerful framework for both research and application building. As a second contribution, we have addressed the problem of building representative extraction task-specific document samples from collections, a step often required by approaches for efficient information extraction. Specifically, we devised fully automatic document sampling techniques for information extraction that can produce better-quality document samples than the state-of-the-art sampling strategies; furthermore, our techniques are substantially more efficient than the existing alternative approaches. To scale the information extraction process to large volumes of text, we have developed approaches that address the efficiency and scalability of the extraction process by focusing the extraction effort on the collections, documents, and sentences worth processing for a given extraction task. For collections, we have studied both (adaptations of) state-of-the art approaches for estimating the number of documents in a collection that lead to the extraction of tuples as well as information extraction-specific approaches. Using these estimations we can identify the collections worth processing and ignore the rest, for efficiency. For documents, we have developed an adaptive document ranking approach that relies on learning-to-rank techniques to prioritize the documents that are likely to produce tuples for an extraction task of choice. Our approach revises the (learned) ranking decisions periodically as the extraction process progresses and new characteristics of the useful documents are revealed. Finally, for sentences, we have developed an approach based on the sparse group selection problem that identifies sentences|modeled as groups of words|that best characterize the extraction task. Beyond identifying sentences worth processing, our approach aims at selecting sentences that lead to the extraction of unseen, novel tuples. Our approaches are lightweight and efficient, and dramatically improve the efficiency and scalability of the information extraction process. We can often complete the extraction task by focusing on just a very small fraction of the available text, namely, the text that contains relevant information for the extraction task at hand. Our approaches therefore constitute a substantial step towards efficient and scalable information extraction over large volumes of text

Position bias in features

The purpose of modeling document relevance for search engines is to rank better in subsequent searches. Document-specific historical click-through rates can be important features in a dynamic ranking system which updates as we accumulate more sample. This paper describes the properties of several such features, and tests them in controlled experiments. Extending the inverse propensity weighting method to documents creates an unbiased estimate of document relevance. This feature can approximate relevance accurately, leading to near-optimal ranking in ideal circumstances. However, it has high variance that is increasing with respect to the degree of position bias. Furthermore, inaccurate position bias estimation leads to poor performance. Under several scenarios this feature can perform worse than biased click-through rates. This paper underscores the need for accurate position bias estimation, and is unique in suggesting simultaneous use of biased and unbiased position bias features