Interactive Constrained Association Rule Mining

We investigate ways to support interactive mining sessions, in the setting of association rule mining. In such sessions, users specify conditions (queries) on the associations to be generated. Our approach is a combination of the integration of querying conditions inside the mining phase, and the incremental querying of already generated associations. We present several concrete algorithms and compare their performance.Comment: A preliminary report on this work was presented at the Second International Conference on Knowledge Discovery and Data Mining (DaWaK 2000

Enabling Graph Analysis Over Relational Databases

Complex interactions and systems can be modeled by analyzing the connections between underlying entities or objects described by a dataset. These relationships form networks (graphs), the analysis of which has been shown to provide tremendous value in areas ranging from retail to many scientific domains. This value is obtained by using various methodologies from network science-- a field which focuses on studying network representations in the real world. In particular "graph algorithms", which iteratively traverse a graph's connections, are often leveraged to gain insights. To take advantage of the opportunity presented by graph algorithms, there have been a variety of specialized graph data management systems, and analysis frameworks, proposed in recent years, which have made significant advances in efficiently storing and analyzing graph-structured data. Most datasets however currently do not reside in these specialized systems but rather in general-purpose relational database management systems (RDBMS). A relational or similarly structured system is typically governed by a schema of varying strictness that implements constraints and is meticulously designed for the specific enterprise. Such structured datasets contain many relationships between the entities therein, that can be seen as latent or "hidden" graphs that exist inherently inside the datasets. However, these relationships can only typically be traversed via conducting expensive JOINs using SQL or similar languages. Thus, in order for users to efficiently traverse these latent graphs to conduct analysis, data needs to be transformed and migrated to specialized systems. This creates barriers that hinder and discourage graph analysis; our vision is to break these barriers. In this dissertation we investigate the opportunities and challenges involved in efficiently leveraging relationships within data stored in structured databases. First, we present GraphGen, a lightweight software layer that is independent from the underlying database, and provides interfaces for graph analysis of data in RDBMSs. GraphGen is the first such system that introduces an intuitive high-level language for specifying graphs of interest, and utilizes in-memory graph representations to tackle the problems associated with analyzing graphs that are hidden inside structured datasets. We show GraphGen can analyze such graphs in orders of magnitude less memory, and often computation time, while eliminating manual Extract-Transform-Load (ETL) effort. Second, we examine how in-memory graph representations of RDBMS data can be used to enhance relational query processing. We present a novel, general framework for executing GROUP BY aggregation over conjunctive queries which avoids materialization of intermediate JOIN results, and wrap this framework inside a multi-way relational operator called Join-Agg. We show that Join-Agg can compute aggregates over a class of relational and graph queries using orders of magnitude less memory and computation time

Portinari: A Data Exploration Tool to Personalize Cervical Cancer Screening

Socio-technical systems play an important role in public health screening programs to prevent cancer. Cervical cancer incidence has significantly decreased in countries that developed systems for organized screening engaging medical practitioners, laboratories and patients. The system automatically identifies individuals at risk of developing the disease and invites them for a screening exam or a follow-up exam conducted by medical professionals. A triage algorithm in the system aims to reduce unnecessary screening exams for individuals at low-risk while detecting and treating individuals at high-risk. Despite the general success of screening, the triage algorithm is a one-size-fits all approach that is not personalized to a patient. This can easily be observed in historical data from screening exams. Often patients rely on personal factors to determine that they are either at high risk or not at risk at all and take action at their own discretion. Can exploring patient trajectories help hypothesize personal factors leading to their decisions? We present Portinari, a data exploration tool to query and visualize future trajectories of patients who have undergone a specific sequence of screening exams. The web-based tool contains (a) a visual query interface (b) a backend graph database of events in patients' lives (c) trajectory visualization using sankey diagrams. We use Portinari to explore diverse trajectories of patients following the Norwegian triage algorithm. The trajectories demonstrated variable degrees of adherence to the triage algorithm and allowed epidemiologists to hypothesize about the possible causes.Comment: Conference paper published at ICSE 2017 Buenos Aires, at the Software Engineering in Society Track. 10 pages, 5 figure

Spatial Data Quality in the IoT Era:Management and Exploitation

Within the rapidly expanding Internet of Things (IoT), growing amounts of spatially referenced data are being generated. Due to the dynamic, decentralized, and heterogeneous nature of the IoT, spatial IoT data (SID) quality has attracted considerable attention in academia and industry. How to invent and use technologies for managing spatial data quality and exploiting low-quality spatial data are key challenges in the IoT. In this tutorial, we highlight the SID consumption requirements in applications and offer an overview of spatial data quality in the IoT setting. In addition, we review pertinent technologies for quality management and low-quality data exploitation, and we identify trends and future directions for quality-aware SID management and utilization. The tutorial aims to not only help researchers and practitioners to better comprehend SID quality challenges and solutions, but also offer insights that may enable innovative research and applications

A Comprehensive Survey on Graph Summarization with Graph Neural Networks

As large-scale graphs become more widespread, more and more computational challenges with extracting, processing, and interpreting large graph data are being exposed. It is therefore natural to search for ways to summarize these expansive graphs while preserving their key characteristics. In the past, most graph summarization techniques sought to capture the most important part of a graph statistically. However, today, the high dimensionality and complexity of modern graph data are making deep learning techniques more popular. Hence, this paper presents a comprehensive survey of progress in deep learning summarization techniques that rely on graph neural networks (GNNs). Our investigation includes a review of the current state-of-the-art approaches, including recurrent GNNs, convolutional GNNs, graph autoencoders, and graph attention networks. A new burgeoning line of research is also discussed where graph reinforcement learning is being used to evaluate and improve the quality of graph summaries. Additionally, the survey provides details of benchmark datasets, evaluation metrics, and open-source tools that are often employed in experimentation settings, along with a discussion on the practical uses of graph summarization in different fields. Finally, the survey concludes with a number of open research challenges to motivate further study in this area.Comment: 20 pages, 4 figures, 3 tables, Journal of IEEE Transactions on Artificial Intelligenc

Probabilistic Inductive Querying Using ProbLog

We study how probabilistic reasoning and inductive querying can be combined within ProbLog, a recent probabilistic extension of Prolog. ProbLog can be regarded as a database system that supports both probabilistic and inductive reasoning through a variety of querying mechanisms. After a short introduction to ProbLog, we provide a survey of the different types of inductive queries that ProbLog supports, and show how it can be applied to the mining of large biological networks.Peer reviewe

Fast, Scalable, and Accurate Algorithms for Time-Series Analysis

Time is a critical element for the understanding of natural processes (e.g., earthquakes and weather) or human-made artifacts (e.g., stock market and speech signals). The analysis of time series, the result of sequentially collecting observations of such processes and artifacts, is becoming increasingly prevalent across scientific and industrial applications. The extraction of non-trivial features (e.g., patterns, correlations, and trends) in time series is a critical step for devising effective time-series mining methods for real-world problems and the subject of active research for decades. In this dissertation, we address this fundamental problem by studying and presenting computational methods for efficient unsupervised learning of robust feature representations from time series. Our objective is to (i) simplify and unify the design of scalable and accurate time-series mining algorithms; and (ii) provide a set of readily available tools for effective time-series analysis. We focus on applications operating solely over time-series collections and on applications where the analysis of time series complements the analysis of other types of data, such as text and graphs. For applications operating solely over time-series collections, we propose a generic computational framework, GRAIL, to learn low-dimensional representations that natively preserve the invariances offered by a given time-series comparison method. GRAIL represents a departure from classic approaches in the time-series literature where representation methods are agnostic to the similarity function used in subsequent learning processes. GRAIL relies on the attractive idea that once we construct the data-to-data similarity matrix most time-series mining tasks can be trivially solved. To overcome scalability issues associated with approaches relying on such matrices, GRAIL exploits time-series clustering to construct a small set of landmark time series and learns representations to reduce the data-to-data matrix to a data-to-landmark points matrix. To demonstrate the effectiveness of GRAIL, we first present domain-independent, highly accurate, and scalable time-series clustering methods to facilitate exploration and summarization of time-series collections. Then, we show that GRAIL representations, when combined with suitable methods, significantly outperform, in terms of efficiency and accuracy, state-of-the-art methods in major time-series mining tasks, such as querying, clustering, classification, sampling, and visualization. Overall, GRAIL rises as a new primitive for highly accurate, yet scalable, time-series analysis. For applications where the analysis of time series complements the analysis of other types of data, such as text and graphs, we propose generic, simple, and lightweight methodologies to learn features from time-varying measurements. Such applications often organize operations over different types of data in a pipeline such that one operation provides input---in the form of feature vectors---to subsequent operations. To reason about the temporal patterns and trends in the underlying features, we need to (i) track the evolution of features over different time periods; and (ii) transform these time-varying features into actionable knowledge (e.g., forecasting an outcome). To address this challenging problem, we propose principled approaches to model time-varying features and study two large-scale, real-world, applications. Specifically, we first study the problem of predicting the impact of scientific concepts through temporal analysis of characteristics extracted from the metadata and full text of scientific articles. Then, we explore the promise of harnessing temporal patterns in behavioral signals extracted from web search engine logs for early detection of devastating diseases. In both applications, combinations of features with time-series relevant features yielded the greatest impact than any other indicator considered in our analysis. We believe that our simple methodology, along with the interesting domain-specific findings that our work revealed, will motivate new studies across different scientific and industrial settings