Technicians and maintenance managers in industrial environments would benefit from automatically extracting entities and relationships from different text data sources such as logs, event reports, and manuals. Extracting components from pieces of text and classifying them to the right failure type is not trivial in the domain specific setting where the vocabulary has specific meaning to the industry or domain, and labeled data set is very small. In this paper we address how to overcome these challenges in named entity recognition and classification of text, and present a way to improve the model iteratively and quickly. This interaction between components and related failures in the system can be represented in a knowledge graph, which enables further investigations such as Root Cause Analysis and Problem Diagnosis.

This poster paper presents an efficient design of deploying anomaly detection service using serverless technology. Our design is motivated by the fact that the workload originating from the service calls are adhoc and reserving the infrastructure upfront is not advisable. To address this, we utilized the emerging serverless platform for executing the incoming training request. Our extensive experimental analysis demonstrate the usefulness of the proposed idea.

Assets in the domain of Internet of Things (IoT) generate time-series data such as sensor readings and alerts. In addition, the assets have associated static data such as the make, model and other manufacturing information. The sensors in the asset components may have implicit relationships with each other, which are not interpretable without domain knowledge. Many problems exist which involve computation of relationships between sensors or subsystems in the asset components. Typically, the number of sensors in a real world asset may range anywhere from tens to thousands of sensors - and in this case, finding relationships between them becomes a highly computationally intensive task. In this paper, we study one such problem of anomaly detection in industrial data based on the functioning of the sensors and their interrelationships in both normal and abnormal conditions. We further demonstrate the issue of run-time and performance complexity in this problem, and present a speed-up strategy using Serverless Computing for parallelization, and demonstrate the usefulness of this method by comparing the speed-up achieved.

We present a novel framework for multivariate time series representation learning based on the transformer encoder architecture. The framework includes an unsupervised pre-training scheme, which can offer substantial performance benefits over fully supervised learning on downstream tasks, both with but even without leveraging additional unlabeled data, i.e., by reusing the existing data samples. Evaluating our framework on several public multivariate time series datasets from various domains and with diverse characteristics, we demonstrate that it performs significantly better than the best currently available methods for regression and classification, even for datasets which consist of only a few hundred training samples. Given the pronounced interest in unsupervised learning for nearly all domains in the sciences and in industry, these findings represent an important landmark, presenting the first unsupervised method shown to push the limits of state-of-the-art performance for multivariate time series regression and classification.

Data quality assessment is an essential process of any data analysis process including machine learning. The process is time-consuming as it involves multiple independent data quality checks that are performed iteratively at scale on evolving data resulting from exploratory data analysis (EDA). Existing solutions that provide computational optimizations for data quality assessment often separate the data structure from its data quality which then requires efforts from users to explicitly maintain state-like information. They demand a certain level of distributed system knowledge to ensure high-level pipeline optimizations from data analysts who should instead be focusing on analyzing the data. We, therefore, propose data-quality-aware dataframes, a data quality management system embedded as part of a data analyst's familiar data structure, such as a Python dataframe. The framework automatically detects changes in datasets' metadata and exploits the context of each of the quality checks to provide efficient data quality assessment on ever-changing data. We demonstrate in our experiment that our approach can reduce the overall data quality evaluation runtime by 40-80% in both local and distributed setups with less than 10% increase in memory usage.

Time series value forecasting using machine learning models utilizing time series features has recently got good attention of Time series analytics community. This paper proposes an automated feature learning mechanisms to filter out most useful features from hundreds of available features for time series prediction problems. The paper further proposes a novel mechanism to dynamically filter features that are most suitable for the given input time series data. With such mechanisms we create pipeline consisting of most useful features for given input data and increases the performance of the prediction model. Our proposed mechanism first, groups well known features for time series analysis, generates and assigns the features importance score using multiple scoring configurations. Once scores are assigned, features are filtered using a threshold that is derived using reference feature score and Critical Difference diagram. The filtered features are subsequently analyzed based on the characteristics of the input dataset. We show using experimental results that our approach of input data based dynamic feature selection improves the overall performance of machine learning models compared to the case where dynamic feature extraction is not applied prior to modeling.

Data Quality (DQ) has been one of the key focuses as Data Analytics and Artificial Intelligence (AI) fields continue to grow. Yet, data quality analysis has mostly been a disjointed, ad-hoc, and cumbersome process in the overall data analysis workflow. There have been ongoing attempts to formalize this process, but the solutions that have come out are not universally applicable. Most of the proposed solutions try to address the problem of data quality from a limited perspective and suc-cessfully address only a subset of all challenges. These solutions fail to translate to other domains due to a lack of structure. In this paper, we present DQLearn, a toolkit for structured data quality learning. We start by presenting the core principle on which we build our library and introduce the four components that provide a solid base to address the needs of the data quality problem. Then, we showcase our automation structure - "Workflows", and the two optimization techniques equipped with it, that help the users to structure their learning problem very easily. Next, we discuss four important scenarios of the DQ Workflows in the overall life-cycle. Finally, we demonstrate the utility of the proposed toolkit with public datasets and show benchmark results from optimization experiments.

Anomaly detection (AD) is very important across several real-world problems in the heavy industries and Internet-of-Things (IoT) domains. Traditional methods so far have categorized anomaly detection into (a) unsupervised, (b) semi-supervised and (c) supervised techniques. A relatively unexplored direction is the development of context aware anomaly detection systems which can build on top of any of these three techniques by using side information. Context can be captured from a different modality such as semantic graphs encoding grouping of sensors governed by the physics of the asset. Process flow diagrams of an operational plant depicting causal relationships between sensors can also provide useful context for ML algorithms. Capturing such semantics by itself can be pretty challenging, however, our paper mainly focuses on, (a) designing and implementing effective anomaly detection pipelines using sparse Gaussian Graphical Models with various statistical distance metrics, and (b) differentiating these pipelines by embedding contextual semantics inferred from graphs so as to obtain better KPIs in practice. The motivation for the latter of these two has been explained above, and the former in particular is well motivated by the relatively mediocre performance of highly parametric deep learning methods for small tabular datasets (compared to images) such as IoT sensor data. In contrast to such traditional automated deep learning (AutoAI) techniques, our anomaly detection system is based on developing semantics-driven industry specific ML pipelines which perform scalable computation evaluating several models to identify the best model. We benchmark our AD method against state-of-the-art AD techniques on publicly available UCI datasets. We also conduct a case study on IoT sensor and semantic data procured from a large thermal energy asset to evaluate the importance of semantics in enhancing our pipelines. In addition, we also provide explainable insights for our model which provide a complete perspective to a reliability engineer.

This paper proposes a verifiable imputation process and an enabling tool for univariate time series. Common ad-hoc and case-specific imputation are not enough to ensure high quality and effective imputation. We adopt the similar verification logic of supervised learning. We use artificial missing sampling as the test set to estimate a set of imputers' performances and use the estimated performances to select the best imputer. To ensure the correctness of selection, we analyze the impact of various factors on estimation accuracy. Those factors are missing rate, size of artificial missing data and patterns, selected imputers, and noise level. We propose a two-step verifiable imputation process to integrate all of the steps. With this process, we can always leverage the most suitable imputer to achieve a high quality of imputation without tedious and error-prone data cleaning efforts. We implement the tool as a Python package, with many imputers with their unique capabilities and a API. We automate the imputation through a standard process, which returns imputed results and detailed rationales of selection along with quality metrics.

Fueled with growth in the fields of Internet of Things (IoT) and Big Data, data has become one of the most valuable assets in today's world. While we are leveraging this data for analyzing complex systems using machine learning and deep learning, a considerable amount of time and effort is spent on addressing data quality issues. If undetected, data quality issues can cause large deviations in the analysis, misleading data scientists. To ease the effort of identifying and addressing data quality challenges, we introduce DQA, a scalable, automated and interactive data quality advisor. In this paper, we describe the DQA framework, provide detailed description of its components and the benefits of integrating it in a data science process. We propose a programmatic approach for implementing the data quality framework which automatically generates dynamic executable graphs for performing data validations fine-tuned for a given dataset. We discuss the use of DQA to build a library of validation checks common to many applications. We provide insight into how DQA addresses many persistence and usability issues which currently make data cleaning a laborious task for data scientists. Finally, we provide a case study of how DQA is implemented in a realworld system and describe the benefits realized.

Cognitive Company Discovery is an application that helps business professionals identify companies of interest to them. The application employs a variety of artificial intelligence and data science techniques to build a corpus of company data, rapidly search the corpus based on implicit and explicit user queries, present the results using visualization techniques that yield insight into areas of interest to the user and to scan vast amounts of news and blog posts to aid users in discovering new companies. The application is currently deployed in a major corporation. A video that demonstrates our system can be found at the following URL: https://player.vimeo.com/video/278031050

Deduplication is a key operation in integrating data from multiple sources. The main challenge in this task is designing a function that can resolve when a pair of records refer to the same entity in spite of various data inconsistencies. Most existing systems use hand-coded functions. One way to overcome the tedium of hand-coding is to train a classifier to distinguish between duplicates and non-duplicates. The success of this method critically hinges on being able to provide a covering and challenging set of training pairs that bring out the subtlety of deduplication function. This is non-trivial because it requires manually searching for various data inconsistencies between any two records spread apart in large lists.We present our design of a learning-based deduplication system that uses a novel method of interactively discovering challenging training pairs using active learning. Our experiments on real-life datasets show that active learning significantly reduces the number of instances needed to achieve high accuracy. We investigate various design issues that arise in building a system to provide interactive response, fast convergence, and interpretable output.