Data Mining Techniques to Understand Textual Data

More than ever, information delivery online and storage heavily rely on text. Billions of texts are produced every day in the form of documents, news, logs, search queries, ad keywords, tags, tweets, messenger conversations, social network posts, etc. Text understanding is a fundamental and essential task involving broad research topics, and contributes to many applications in the areas text summarization, search engine, recommendation systems, online advertising, conversational bot and so on. However, understanding text for computers is never a trivial task, especially for noisy and ambiguous text such as logs, search queries. This dissertation mainly focuses on textual understanding tasks derived from the two domains, i.e., disaster management and IT service management that mainly utilizing textual data as an information carrier. Improving situation awareness in disaster management and alleviating human efforts involved in IT service management dictates more intelligent and efficient solutions to understand the textual data acting as the main information carrier in the two domains. From the perspective of data mining, four directions are identified: (1) Intelligently generate a storyline summarizing the evolution of a hurricane from relevant online corpus; (2) Automatically recommending resolutions according to the textual symptom description in a ticket; (3) Gradually adapting the resolution recommendation system for time correlated features derived from text; (4) Efficiently learning distributed representation for short and lousy ticket symptom descriptions and resolutions. Provided with different types of textual data, data mining techniques proposed in those four research directions successfully address our tasks to understand and extract valuable knowledge from those textual data. My dissertation will address the research topics outlined above. Concretely, I will focus on designing and developing data mining methodologies to better understand textual information, including (1) a storyline generation method for efficient summarization of natural hurricanes based on crawled online corpus; (2) a recommendation framework for automated ticket resolution in IT service management; (3) an adaptive recommendation system on time-varying temporal correlated features derived from text; (4) a deep neural ranking model not only successfully recommending resolutions but also efficiently outputting distributed representation for ticket descriptions and resolutions

Ticket Automation: an Insight into Current Research with Applications to Multi-level Classification Scenarios

odern service providers often have to deal with large amounts of customer requests, which they need to act upon in a swift and effective manner to ensure adequate support is provided. In this context, machine learning algorithms are fundamental in streamlining support ticket processing workflows. However, a large part of current approaches is still based on traditional Natural Language Processing approaches without fully exploiting the latest advancements in this field. In this work, we aim to provide an overview of support Ticket Automation, what recent proposals are being made in this field, and how well some of these methods can generalize to new scenarios and datasets. We list the most recent proposals for these tasks and examine in detail the ones related to Ticket Classification, the most prevalent of them. We analyze commonly utilized datasets and experiment on two of them, both characterized by a two-level hierarchy of labels, which are descriptive of the ticket’s topic at different levels of granularity. The first is a collection of 20,000 customer complaints, and the second comprises 35,000 issues crawled from a bug reporting website. Using this data, we focus on topically classifying tickets using a pre-trained BERT language model. The experimental section of this work has two objectives. First, we demonstrate the impact of different document representation strategies on classification performance. Secondly, we showcase an effective way to boost classification by injecting information from the hierarchical structure of the labels into the classifier. Our findings show that the choice of the embedding strategy for ticket embeddings considerably impacts classification metrics on our datasets: the best method improves by more than 28% in F1- score over the standard strategy. We also showcase the effectiveness of hierarchical information injection, which further improves the results. In the bugs dataset, one of our multi-level models (ML-BERT) outperforms the best baseline by up to 5.7% in F1-score and 5.4% in accuracy

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field

Machine Learning for the New York City Power Grid

Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These models can be used directly by power companies to assist with prioritization of maintenance and repair work. Specialized versions of this process are used to produce (1) feeder failure rankings, (2) cable, joint, terminator, and transformer rankings, (3) feeder Mean Time Between Failure (MTBF) estimates, and (4) manhole events vulnerability rankings. The process in its most general form can handle diverse, noisy, sources that are historical (static), semi-real-time, or real-time, incorporates state-of-the-art machine learning algorithms for prioritization (supervised ranking or MTBF), and includes an evaluation of results via cross-validation and blind test. Above and beyond the ranked lists and MTBF estimates are business management interfaces that allow the prediction capability to be integrated directly into corporate planning and decision support; such interfaces rely on several important properties of our general modeling approach: that machine learning features are meaningful to domain experts, that the processing of data is transparent, and that prediction results are accurate enough to support sound decision making. We discuss the challenges in working with historical electrical grid data that were not designed for predictive purposes. The “rawness” of these data contrasts with the accuracy of the statistical models that can be obtained from the process; these models are sufficiently accurate to assist in maintaining New York City's electrical grid

Automatic Detection of Mass Outages in Radio Access Networks

Fault management in mobile networks is required for detecting, analysing, and fixing problems appearing in the mobile network. When a large problem appears in the mobile network, multiple alarms are generated from the network elements. Traditionally Network Operations Center (NOC) process the reported failures, create trouble tickets for problems, and perform a root cause analysis. However, alarms do not reveal the root cause of the failure, and the correlation of alarms is often complicated to determine. If the network operator can correlate alarms and manage clustered groups of alarms instead of separate ones, it saves costs, preserves the availability of the mobile network, and improves the quality of service. Operators may have several electricity providers and the network topology is not correlated with the electricity topology. Additionally, network sites and other network elements are not evenly distributed across the network. Hence, we investigate the suitability of a density-based clustering methods to detect mass outages and perform alarm correlation to reduce the amount of created trouble tickets. This thesis focuses on assisting the root cause analysis and detecting correlated power and transmission failures in the mobile network. We implement a Mass Outage Detection Service and form a custom density-based algorithm. Our service performs alarm correlation and creates clusters of possible power and transmission mass outage alarms. We have filed a patent application based on the work done in this thesis. Our results show that we are able to detect mass outages in real time from the data streams. The results also show that detected clusters reduce the number of created trouble tickets and help reduce of the costs of running the network. The number of trouble tickets decreases by 4.7-9.3% for the alarms we process in the service in the tested networks. When we consider only alarms included in the mass outage groups, the reduction is over 75%. Therefore continuing to use, test, and develop implemented Mass Outage Detection Service is beneficial for operators and automated NOC