Query-Driven Learning for Next Generation Predictive Modeling & Analytics

As data-size is increasing exponentially, new paradigm shifts have to emerge allowing fast exploitation of data by every- body. Large-scale predictive analytics is restricted to wealthy organizations as small-scale enterprises (SMEs) struggle to compete and are inundated by the sheer monetary cost of either procuring data infrastructures or analyzing datasets over the Cloud. The aim of this work is to study mechanisms which can democratize analytics, in the sense of making them affordable, while at the same time ensuring high efficiency, scalability, and accuracy. The crux of this proposal lies in developing query-driven solutions that can be used off the Cloud thus minimizing costs. Our query-driven approach will learn and adapt on-the-fly machine learning models, based solely on query-answer interactions, which can be used for answering analytical queries. In this abstract we describe the methodology followed for the implementation and evaluation of the system designed

Scalable aggregation predictive analytics: a query-driven machine learning approach

We introduce a predictive modeling solution that provides high quality predictive analytics over aggregation queries in Big Data environments. Our predictive methodology is generally applicable in environments in which large-scale data owners may or may not restrict access to their data and allow only aggregation operators like COUNT to be executed over their data. In this context, our methodology is based on historical queries and their answers to accurately predict ad-hoc queries’ answers. We focus on the widely used set-cardinality, i.e., COUNT, aggregation query, as COUNT is a fundamental operator for both internal data system optimizations and for aggregation-oriented data exploration and predictive analytics. We contribute a novel, query-driven Machine Learning (ML) model whose goals are to: (i) learn the query-answer space from past issued queries, (ii) associate the query space with local linear regression & associative function estimators, (iii) define query similarity, and (iv) predict the cardinality of the answer set of unseen incoming queries, referred to the Set Cardinality Prediction (SCP) problem. Our ML model incorporates incremental ML algorithms for ensuring high quality prediction results. The significance of contribution lies in that it (i) is the only query-driven solution applicable over general Big Data environments, which include restricted-access data, (ii) offers incremental learning adjusted for arriving ad-hoc queries, which is well suited for query-driven data exploration, and (iii) offers a performance (in terms of scalability, SCP accuracy, processing time, and memory requirements) that is superior to data-centric approaches. We provide a comprehensive performance evaluation of our model evaluating its sensitivity, scalability and efficiency for quality predictive analytics. In addition, we report on the development and incorporation of our ML model in Spark showing its superior performance compared to the Spark’s COUNT method

Query-driven learning for automating exploratory analytics in large-scale data management systems

As organizations collect petabytes of data, analysts spend most of their time trying to extract insights. Although data analytic systems have become extremely efficient and sophisticated, the data exploration phase is still a laborious task with high productivity, monetary and mental costs. This dissertation presents the Query-Driven learning methodology in which multiple systems/frameworks are introduced to address the need of more efficient methods to analyze large data sets. Countless queries are executed daily, in large deployments, and are often left unexploited but we believe they are of immense value. This work describes how Machine Learning can be used to expedite the data exploration process by (a) estimating the results of aggregate queries (b) explaining data spaces through interpretable Machine Learning models (c) identifying data space regions that could be of interest to the data analyst. Compared to related work in all the associated domains, the proposed solutions do not utilize any of the underlying data. Because of that, they are extremely efficient, decoupled from underlying infrastructure and can easily be adapted. This dissertation is a first account of how the Query-Driven methodology can be effectively used to expedite the data exploration process focusing solely on extracting knowledge from queries and not from data

SuRF: Identification of Interesting Data Regions with Surrogate Models

Several data mining tasks focus on repeatedly inspecting multidimensional data regions summarized by a statistic. The value of this statistic (e.g., region-population sizes, order moments) is used to classify the region’s interesting-ness. These regions can be naively extracted from the entire dataspace – however, this is extremely time-consuming and compute-resource demanding. This paper studies the reverse problem: analysts provide a cut-off value for a statistic of interest and in turn our proposed framework efficiently identifies multidimensional regions whose statistic exceeds (or is below) the given cut-off value (according to user’s needs). However, as data dimensions and size increase, such task inevitably becomes laborious and costly. To alleviate this cost, our solution, coined SuRF (SUrrogate Region Finder), leverages historical region evaluations to train surrogate models that learn to approximate the distribution of the statistic of interest. It then makes use of evolutionary multi-modal optimization to effectively and efficiently identify regions of interest regardless of data size and dimensionality. The accuracy, efficiency, and scalability of our approach are demonstrated with experiments using synthetic and real-world datasets and compared with other methods

Aggregate Query Prediction under Dynamic Workloads

Large organizations have seamlessly incorporated data-driven decision making in their operations. However, as data volumes increase, expensive big data infrastructures are called to rescue. In this setting, analytics tasks become very costly in terms of query response time, resource consumption, and money in cloud deployments, especially when base data are stored across geographically distributed data centers. Therefore, we introduce an adaptive Machine Learning mechanism which is light-weight, stored client-side, can estimate the answers of a variety of aggregate queries and can avoid the big data backend. The estimations are performed in milliseconds and are inexepensive as the mechanism learns from past analytical-query patterns. However, as analytic queries are ad-hoc and analysts’ interests change over time we develop solutions that can swiftly and accurately detect such changes and adapt to new query patterns. The capabilities of our approach are demonstrated using extensive evaluation with real and synthetic datasets

Explaining Aggregates for Exploratory Analytics

Analysts wishing to explore multivariate data spaces, typically pose queries involving selection operators, i.e., range or radius queries, which define data subspaces of possible interest and then use aggregation functions, the results of which determine their exploratory analytics interests. However, such aggregate query (AQ) results are simple scalars and as such, convey limited information about the queried subspaces for exploratory analysis.We address this shortcoming aiding analysts to explore and understand data subspaces by contributing a novel explanation mechanism coined XAXA: eXplaining Aggregates for eXploratory Analytics. XAXA’s novel AQ explanations are represented using functions obtained by a three-fold joint optimization problem. Explanations assume the form of a set of parametric piecewise-linear functions acquired through a statistical learning model. A key feature of the proposed solution is that model training is performed by only monitoring AQs and their answers on-line. In XAXA, explanations for future AQs can be computed without any database (DB) access and can be used to further explore the queried data subspaces, without issuing any more queries to the DB. We evaluate the explanation accuracy and efficiency of XAXA through theoretically grounded metrics over real-world and synthetic datasets and query workloads

Large-scale data exploration using explanatory regression functions

Analysts wishing to explore multivariate data spaces, typically issue queries involving selection operators, i.e., range or equality predicates, which define data subspaces of potential interest. Then, they use aggregation functions, the results of which determine a subspace’s interestingness for further exploration and deeper analysis. However, Aggregate Query (AQ) results are scalars and convey limited information and explainability about the queried subspaces for enhanced exploratory analysis. Analysts have no way of identifying how these results are derived or how they change w.r.t query (input) parameter values. We address this shortcoming by aiding analysts to explore and understand data subspaces by contributing a novel explanation mechanism based on machine learning. We explain AQ results using functions obtained by a three-fold joint optimization problem which assume the form of explainable piecewise-linear regression functions. A key feature of the proposed solution is that the explanation functions are estimated using past executed queries. These queries provide a coarse grained overview of the underlying aggregate function (generating the AQ results) to be learned. Explanations for future, previously unseen AQs can be computed without accessing the underlying data and can be used to further explore the queried data subspaces, without issuing more queries to the backend analytics engine. We evaluate the explanation accuracy and efficiency through theoretically grounded metrics over real-world and synthetic datasets and query workloads

Explaining aggregates for exploratory analytics

Analysts wishing to explore multivariate data spaces, typically pose queries involving selection operators, i.e., range or radius queries, which define data subspaces of possible interest and then use aggregation functions, the results of which determine their exploratory analytics interests. However, such aggregate query (AQ) results are simple scalars and as such, convey limited information about the queried subspaces for exploratory analysis. We address this shortcoming aiding analysts to explore and understand data subspaces by contributing a novel explanation mechanism coined XAXA: eXplaining Aggregates for eXploratory Analytics. XAXA’s novel AQ explanations are repre- sented using functions obtained by a three-fold joint optimization problem. Explanations assume the form of a set of parametric piecewise-linear functions acquired through a statistical learning model. A key feature of the proposed solution is that model training is performed by only monitoring AQs and their answers on-line. In XAXA, explanations for future AQs can be computed without any database (DB) access and can be used to further explore the queried data subspaces, without issuing any more queries to the DB. We evaluate the explanation accuracy and efficiency of XAXA through theoretically grounded metrics over real-world and synthetic datasets and query workloads

TSMF: Network Latency Estimation Using Matrix Factorization and Time Series Forecasting

The ability to accurately estimate end-to-end network latencies is extremely important for many services, from overlay network formation to Edge computing and 5G. Research in Network Coordinate Systems (NCS) has over the years focused on providing such estimates while conserving network resources by avoiding excessive probing. However, Internet latencies are inherently unstable and estimates produced by existing NCS's are shown to quickly become obsolete. In this paper, we devise TSMF, a novel NCS method based on an ensemble of Time-Series Forecasting and Matrix Factorization (MF). Fusing the two approaches results in a model that takes advantage of the low-rank structure of end-to-end latencies and temporal correlations with past measurements. In addition, TSMF can forecast future end-to-end latencies which has been impossible using existing NCS approaches. Our results demonstrate that TSMF outperforms Euclidean and MF-based NCS's with up to 6× less relative error in predicting end-to-end latencies. We also demonstrate the accuracy of TSMF in forecasting future end-to-end latencies, and its consequent suitability for services such as web-service recommendation