Structure-Aware Sampling: Flexible and Accurate Summarization

In processing large quantities of data, a fundamental problem is to obtain a summary which supports approximate query answering. Random sampling yields flexible summaries which naturally support subset-sum queries with unbiased estimators and well-understood confidence bounds. Classic sample-based summaries, however, are designed for arbitrary subset queries and are oblivious to the structure in the set of keys. The particular structure, such as hierarchy, order, or product space (multi-dimensional), makes range queries much more relevant for most analysis of the data. Dedicated summarization algorithms for range-sum queries have also been extensively studied. They can outperform existing sampling schemes in terms of accuracy on range queries per summary size. Their accuracy, however, rapidly degrades when, as is often the case, the query spans multiple ranges. They are also less flexible - being targeted for range sum queries alone - and are often quite costly to build and use. In this paper we propose and evaluate variance optimal sampling schemes that are structure-aware. These summaries improve over the accuracy of existing structure-oblivious sampling schemes on range queries while retaining the benefits of sample-based summaries: flexible summaries, with high accuracy on both range queries and arbitrary subset queries

Data science applications to connected vehicles: Key barriers to overcome

The connected vehicles will generate huge amount of pervasive and real time data, at very high frequencies. This poses new challenges for Data science. How to analyse these data and how to address short-term and long-term storage are some of the key barriers to overcome.JRC.C.6-Economics of Climate Change, Energy and Transpor

Load Balance and Resource Efficiency in Communication Networks

Network management is critical for today’s network. This study investigates both load balancing and resource efficiency in network management. For load balancing, one unfavorable situation is that the active traffic uses a portion of the equal-cost paths instead of all. The root causes of load imbalance are not easily identified and located by network operators. Most research work related in this area concerns the design of load balancing mechanisms or network-wide troubleshooting that does not specify the causes of load imbalance. In this study, we describe a computational framework based on network measurements to identify the correlation mechanism causing the load imbalance. We also describe a novel framework based on Coprime to mitigate the load imbalance brought by hash correlations. In evaluation based on real network trace data and topologies, we have proved that we can reduces the error (CV or K-S statistic) by at least one magnitude. For resource efficiency, today’s network demands an increasing switch memory to support the essential functions, such as forwarding, monitoring, etc. However, the cache memory is restricted when processing data streams in which the input is presented as a sequence of items and can be examined in only a few passes (typically just one). This study introduces a new single-pass reservoir weighted-sampling stream aggregation algorithm, Priority-Based Aggregation (PBA). A naive approach to order sample regardless of key then aggregate the results is hopelessly inefficient. In distinction, our proposed algorithm uses a single persistent random variable across the lifetime of each key in the cache and maintains unbiased estimates of the key aggregates that can be queried at any point in the stream. Concerning statistical properties, we prove that PBA provides unbiased estimates of the true aggregates. We analyze the computational complexity of PBA and its variants and provide a detailed evaluation of its accuracy on synthetic and trace data. In addition to sampling, this study also considers placing classification rules into switches from various network functions. While much work has focused on compressing the rules, most of this work proposes solutions operating in the memory of a single switch. Instead, this study proposed a collaborative approach encompassing switches and network functions. This architecture enables trade-off between usage of (expensive) switch memory and (cheaper) downstream network bandwidth and network function resources. Our system can reduce memory usage significantly compared to strawman approaches as demonstrated with extensive simulations and prototype evaluation with real traffic traces and rules

Get the Most out of Your Sample: Optimal Unbiased Estimators using Partial Information

Random sampling is an essential tool in the processing and transmission of data. It is used to summarize data too large to store or manipulate and meet resource constraints on bandwidth or battery power. Estimators that are applied to the sample facilitate fast approximate processing of queries posed over the original data and the value of the sample hinges on the quality of these estimators. Our work targets data sets such as request and traffic logs and sensor measurements, where data is repeatedly collected over multiple {\em instances}: time periods, locations, or snapshots. We are interested in queries that span multiple instances, such as distinct counts and distance measures over selected records. These queries are used for applications ranging from planning to anomaly and change detection. Unbiased low-variance estimators are particularly effective as the relative error decreases with the number of selected record keys. The Horvitz-Thompson estimator, known to minimize variance for sampling with "all or nothing" outcomes (which reveals exacts value or no information on estimated quantity), is not optimal for multi-instance operations for which an outcome may provide partial information. We present a general principled methodology for the derivation of (Pareto) optimal unbiased estimators over sampled instances and aim to understand its potential. We demonstrate significant improvement in estimate accuracy of fundamental queries for common sampling schemes.Comment: This is a full version of a PODS 2011 pape

Algorithmic Techniques for Processing Data Streams

We give a survey at some algorithmic techniques for processing data streams. After covering the basic methods of sampling and sketching, we present more evolved procedures that resort on those basic ones. In particular, we examine algorithmic schemes for similarity mining, the concept of group testing, and techniques for clustering and summarizing data streams

Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery

Network analysis and graph mining play a prominent role in providing insights and studying phenomena across various domains, including social, behavioral, biological, transportation, communication, and financial domains. Across all these domains, networks arise as a natural and rich representation for data. Studying these real-world networks is crucial for solving numerous problems that lead to high-impact applications. For example, identifying the behavior and interests of users in online social networks (e.g., viral marketing), monitoring and detecting virus outbreaks in human contact networks, predicting protein functions in biological networks, and detecting anomalous behavior in computer networks. A key characteristic of these networks is that their complex structure is massive and continuously evolving over time, which makes it challenging and computationally intensive to analyze, query, and model these networks in their entirety. In this dissertation, we propose sampling as well as fast, efficient, and scalable methods for network analysis and mining in both static and streaming graphs

Efficient Algorithms to Compute Hierarchical Summaries from Big Data Streams

Many data stream applications have hierarchical data; containing time, geographic locations, product information, clickstreams, server logs, IP addresses. A hierarchical summary of such volumous data offers multiple advantages including compactness, quick understanding, and abstraction. The goal of this thesis is to design algorithmic approaches for summarizing hierarchical data streams. First, this thesis provides a theoretical analysis of the benchmark hierarchical heavy hitters' algorithms and uncovers their shortcomings such as requiring high theoretical memory, updates and coverage problem. To address these shortcomings, this thesis proposes efficient algorithms which offer deterministic estimation accuracy using O(η/ε) worst-case memory and O(η) worst-case time complexity per item, where ε ∈ [0,1] is a user defined parameter and η is a small constant derived from the data. The proposed hierarchical heavy hitters' algorithms are shown to have improved significantly over existing algorithms both theoretically as well as empirically. Next, this thesis introduces a new concept called hierarchically correlated heavy hitters, which is different from existing hierarchical summarization techniques. The thesis provides a formal definition of the proposed concept and compares it with existing hierarchical summarization approaches both at definition level and empirically. It also proposes an efficient hierarchy-aware algorithm for computing hierarchically correlated heavy hitters. The proposed algorithm offers deterministic estimation accuracy using O(η / (ε_p * ε_s )) worst-case memory and O(η) worst-case time complexity per item, where η is as defined previously, and ε_p ∈ [0,1], ε_s ∈ [0,1] are other user defined parameters. Finally, the thesis proposes a special hierarchical data structure and algorithm to summarize spatiotemporal data. It can be used to extract interesting and useful patterns from high-speed spatiotemporal data streams at multiple spatial and temporal granularities. Theoretical and empirical analysis are provided, which show that the proposed data structure is very efficient concerning data storage and response to queries. It updates a single item in O(1) time and responds to a point query in O(1) time. Importantly, the memory requirement of the proposed data structure is independent of the size of the data and only depends on user-supplied parameters ψ ⃗ and φ ⃗. In summary, this thesis provides a general framework consisting of a set of algorithms and data structures to compute hierarchical summaries of the big data streams. All of the proposed algorithms exploit a lattice structure built from the hierarchical attributes of the data to compute different hierarchical summaries, which can be used to address various data analytic issues in many emerging applications