Temporal Information in Data Science: An Integrated Framework and its Applications

Data science is a well-known buzzword, that is in fact composed of two distinct keywords, i.e., data and science. Data itself is of great importance: each analysis task begins from a set of examples. Based on such a consideration, the present work starts with the analysis of a real case scenario, by considering the development of a data warehouse-based decision support system for an Italian contact center company. Then, relying on the information collected in the developed system, a set of machine learning-based analysis tasks have been developed to answer specific business questions, such as employee work anomaly detection and automatic call classification. Although such initial applications rely on already available algorithms, as we shall see, some clever analysis workflows had also to be developed. Afterwards, continuously driven by real data and real world applications, we turned ourselves to the question of how to handle temporal information within classical decision tree models. Our research brought us the development of J48SS, a decision tree induction algorithm based on Quinlan's C4.5 learner, which is capable of dealing with temporal (e.g., sequential and time series) as well as atemporal (such as numerical and categorical) data during the same execution cycle. The decision tree has been applied into some real world analysis tasks, proving its worthiness. A key characteristic of J48SS is its interpretability, an aspect that we specifically addressed through the study of an evolutionary-based decision tree pruning technique. Next, since a lot of work concerning the management of temporal information has already been done in automated reasoning and formal verification fields, a natural direction in which to proceed was that of investigating how such solutions may be combined with machine learning, following two main tracks. First, we show, through the development of an enriched decision tree capable of encoding temporal information by means of interval temporal logic formulas, how a machine learning algorithm can successfully exploit temporal logic to perform data analysis. Then, we focus on the opposite direction, i.e., that of employing machine learning techniques to generate temporal logic formulas, considering a natural language processing scenario. Finally, as a conclusive development, the architecture of a system is proposed, in which formal methods and machine learning techniques are seamlessly combined to perform anomaly detection and predictive maintenance tasks. Such an integration represents an original, thrilling research direction that may open up new ways of dealing with complex, real-world problems.Data science is a well-known buzzword, that is in fact composed of two distinct keywords, i.e., data and science. Data itself is of great importance: each analysis task begins from a set of examples. Based on such a consideration, the present work starts with the analysis of a real case scenario, by considering the development of a data warehouse-based decision support system for an Italian contact center company. Then, relying on the information collected in the developed system, a set of machine learning-based analysis tasks have been developed to answer specific business questions, such as employee work anomaly detection and automatic call classification. Although such initial applications rely on already available algorithms, as we shall see, some clever analysis workflows had also to be developed. Afterwards, continuously driven by real data and real world applications, we turned ourselves to the question of how to handle temporal information within classical decision tree models. Our research brought us the development of J48SS, a decision tree induction algorithm based on Quinlan's C4.5 learner, which is capable of dealing with temporal (e.g., sequential and time series) as well as atemporal (such as numerical and categorical) data during the same execution cycle. The decision tree has been applied into some real world analysis tasks, proving its worthiness. A key characteristic of J48SS is its interpretability, an aspect that we specifically addressed through the study of an evolutionary-based decision tree pruning technique. Next, since a lot of work concerning the management of temporal information has already been done in automated reasoning and formal verification fields, a natural direction in which to proceed was that of investigating how such solutions may be combined with machine learning, following two main tracks. First, we show, through the development of an enriched decision tree capable of encoding temporal information by means of interval temporal logic formulas, how a machine learning algorithm can successfully exploit temporal logic to perform data analysis. Then, we focus on the opposite direction, i.e., that of employing machine learning techniques to generate temporal logic formulas, considering a natural language processing scenario. Finally, as a conclusive development, the architecture of a system is proposed, in which formal methods and machine learning techniques are seamlessly combined to perform anomaly detection and predictive maintenance tasks. Such an integration represents an original, thrilling research direction that may open up new ways of dealing with complex, real-world problems

'Call' Centres to 'Contact' Centres: Shifting Paradigms of Customer Service Systems and Research

This paper explores and compares the existing paradigm of 'call centres' as simplistic service functions underpinned by Taylorism with, the emer-gence of 'contact centres' as complex customer service systems. Such emergence has been briefly highlighted in the literature however, with little attention to the additional complexity and challenges on service design and delivery as a result of this shift. Through examination of literature and in-depth conversations with practitioners, the research has found that there is a further scope of exploration of contact centres beyond service delivery channels. Organisations have to re-consider service design and its implications on service management through fresh perspectives

Emergency Triage, Treat, and Transportation Model (ET3) if Successfully Implemented in North Carolina: A Simulation Based on 2017 Medicare Billing Data

Emergency department (ED) visits are increasing and a growing number of non-emergency patients are using EMS for non-urgent transportation to EDs. The costs of ED visits far exceed the costs of physician office visits and a significant number of patients are transported to EDs by EMS for low-acuity visits that have the potential to be seen in lower cost care settings. The objective of this study was to calculate potential cost savings from diverting EMS transports from traditional ED destinations to physician offices due to implementation of the ET3 Model. The (2017) Medicare 5% Limited Data Set and 2017 NC HCUP State Emergency Department Database were used to extract all records for Medicare beneficiaries, Medicaid beneficiaries, private payers, and other payers in North Carolina. All medical transportation bills associated with ambulance transport and low-acuity ED visits resulting in a discharge to home outcome were analyzed for cost savings related to ED charges and traditional office charges. With full implementation of ET3 in North Carolina, the potential annual Medicare savings is $3,240,762 with annual savings related to other payers of$5,330,024, (Medicaid), $52,911,342 (private) and$8,350,396 (other payers). This represents a cumulative cost savings of $69,832,524

Nonresponse in survey research: proceedings of the Eighth International Workshop on Household Survey Nonresponse, 24-16 September 1997

"This volume, the fourth in the ZUMA-Nachrichten Spezial series on methodological issues in empirical social science research, takes up issues of nonresponse. Nonresponse, that is, the failure to obtain measurements from all targeted members of a survey sample, is a problem which confronts many survey organizations in different parts of the world. The papers in this volume discuss nonresponse from different perspectives: they describe efforts undertaken for individual surveys and procedures employed in different countries to deal with nonresponse, analyses of the role of interviewers, the use of advance letters, incentives, etc. to reduce nonresponse rates, analyses of the correlates and consequences of nonresponse, and descriptions of post-survey statistical adjustments to compensate for nonresponse. All the contributions are based on presentations made at the '8th International Workshop on Household Survey Nonresponse'." (author's abstract). Contents: Larry Swain, David Dolson: Current issues in household survey nonresponse at Statistics Canada (1-22); Preston Jay Waite, Vicki J. Huggins, Stephen P. Mack: Assessment of efforts to reduce nonresponse bias: 1996 Survey of Income and Program Participation (SIPP) (23-44); Clyde Tucker, Brian A. Harris-Kojetin: The impact of nonresponse on the unemployment rate in the Current Population Survey (CPS) (45-54); Claudio Ceccarelli, Giuliana Coccia, Fabio Crescenzi: An evaluation of unit nonresponse bias in the Italian households budget survey (55-64); Eva Havasi and Adam Marton: Nonresponse in the 1996 income survey (supplement to the microcensus) (65-74); Metka Zaletel, Vasja Vehovar: The stability of nonresponse rates according to socio-demographie categories (75-84); John King: Understanding household survey nonresponse through geo-demographic coding schemes (85-96); Hakan L. Lindström: Response distributions when TDE is introduced (97-112); Vesa Kuusela: A survey on telephone coverage in Finland (113-120); Malka Kantorowitz: Is it true that nonresponse rates in a panel survey increase when supplement surveys are annexed? (121-138); Vasja Vehovar, Katja Lozar: How many mailings are enough? (139-150); Amanda White, Jean Martin, Nikki Bennett, Stephanie Freeth: Improving advance letters for major government surveys (151-172); Joop Hox, Edith de Leeuw, Ger Snijkers: Fighting nonresponse in telephone interviews: successful interviewer tactics (173-186); Patrick Sturgis, Pamela Campanelli: The effect of interviewer persuasion strategies on refusal rates in household surveys (187-200); Janet Harkness, Peter Mohler, Michael Schneid, Bernhard Christoph: Incentives in two German mail surveys 1996/97 and 1997 (201-218); David Cantor, Bruce Allen, Patricia Cunningham, J. Michael Brick, Renee Slobasky, Pamela Giambo, Jenny Kenny: Promised incentivcs on a random digit dial survey (219-228); Eleanor Singer; John van Hoewyk, Mary P. Maher: Does the payment of incentives create expectation effects? (229-238); Edith de Leeuw, Joop Hox, Ger Snijkers, Wim de Heer: Interviewer opinions, attitudes and strategies regarding survey participation and their effect on response (239-248); Geert Loosveldt, Ann Carton, Jan Pickery: The effect of interviewer and respondent characteristics on refusals in a panel survey (249-262); Brian A. Harris-Kojetin, Clyde Tucker: Longitudinal nonresponse in the Current Population Survey (CPS) (263-272); Ulrich Rendtel, Felix Büchel: A bootstrap strategy for the detection of a panel attrition bias in a household panel with an application to the German Socio-Economic Panel (GSOEP) (273-284); Seppo Laaksonen: Regression-based nearest neighbour hot decking (285-298); Rajendra P. Singh, Rita J. Petroni: Handling of household and item nonresponse in surveys (299-316); Susanne Raessler, Karlheinz Fleischer: Aspects concerning data fusion techniques (317-334); Siegfried Gabler, Sabine Häder: A conditional minimax estimator for treating nonresponse (335-349)

Productivity Measurement of Call Centre Agents using a Multimodal Classification Approach

Call centre channels play a cornerstone role in business communications and transactions, especially in challenging business situations. Operations’ efficiency, service quality, and resource productivity are core aspects of call centres’ competitive advantage in rapid market competition. Performance evaluation in call centres is challenging due to human subjective evaluation, manual assortment to massive calls, and inequality in evaluations because of different raters. These challenges impact these operations' efficiency and lead to frustrated customers. This study aims to automate performance evaluation in call centres using various deep learning approaches. Calls recorded in a call centre are modelled and classified into high- or low-performance evaluations categorised as productive or nonproductive calls. The proposed conceptual model considers a deep learning network approach to model the recorded calls as text and speech. It is based on the following: 1) focus on the technical part of agent performance, 2) objective evaluation of the corpus, 3) extension of features for both text and speech, and 4) combination of the best accuracy from text and speech data using a multimodal structure. Accordingly, the diarisation algorithm extracts that part of the call where the agent is talking from which the customer is doing so. Manual annotation is also necessary to divide the modelling corpus into productive and nonproductive (supervised training). Krippendorff’s alpha was applied to avoid subjectivity in the manual annotation. Arabic speech recognition is then developed to transcribe the speech into text. The text features are the words embedded using the embedding layer. The speech features make several attempts to use the Mel Frequency Cepstral Coefficient (MFCC) upgraded with Low-Level Descriptors (LLD) to improve classification accuracy. The data modelling architectures for speech and text are based on CNNs, BiLSTMs, and the attention layer. The multimodal approach follows the generated models to improve performance accuracy by concatenating the text and speech models using the joint representation methodology. The main contributions of this thesis are: • Developing an Arabic Speech recognition method for automatic transcription of speech into text. • Drawing several DNN architectures to improve performance evaluation using speech features based on MFCC and LLD. • Developing a Max Weight Similarity (MWS) function to outperform the SoftMax function used in the attention layer. • Proposing a multimodal approach for combining the text and speech models for best performance evaluation

Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is the most common type of leukemia. The Cancer Genome Atlas Research Network has demonstrated the increasing genomic complexity of acute myeloid leukemia (AML). In addition, the network has facilitated our understanding of the molecular events leading to this deadly form of malignancy for which the prognosis has not improved over past decades. AML is a highly heterogeneous disease, and cytogenetics and molecular analysis of the various chromosome aberrations including deletions, duplications, aneuploidy, balanced reciprocal translocations and fusion of transcription factor genes and tyrosine kinases has led to better understanding and identification of subgroups of AML with different prognoses. Furthermore, molecular classification based on mRNA expression profiling has facilitated identification of novel subclasses and defined high-, poor-risk AML based on specific molecular signatures. However, despite increased understanding of AML genetics, the outcome for AML patients whose number is likely to rise as the population ages, has not changed significantly. Until it does, further investigation of the genomic complexity of the disease and advances in drug development are needed. In this review, leading AML clinicians and research investigators provide an up-to-date understanding of the molecular biology of the disease addressing advances in diagnosis, classification, prognostication and therapeutic strategies that may have significant promise and impact on overall patient survival