With the increasing impetus on globalization, workplace modernization, and employee welfare, modern organizations are focusing more and more resources in developing their pool of human capital. Employee attrition poses a major challenge for organizations - be it in terms of operational cost or loss of talent. We present CLARA (CLustering for Analysis and Remedial of Attrition), a deployed end-to-end system applying descriptive, predictive, and prescriptive analytics, providing as output actionable remedial actions to be used by HR departments to improve employee retention. We propose a coupling between clustering and frequent pattern based scoring measure to identify candidate employees at a high risk of attrition, and subsequently recommend suggestive actions to improve their retention. Using publicly available IBM human resource (HR) dataset, we show that CLARA demonstrates comparable performance (compared to state-of-the-art techniques) in identifying such “high risk” employees. We further validate the quality of the recommendations provided by the framework in reducing the overall rate of human capital loss, and discuss the real-life implementation of the framework within our organization. CLARA attains up to 65% precision, on IBM HR dataset, in predicting employee attrition and, thanks to the remedial actions, up to 22.5% reduction in the predictive attrition score for the top-5 identified employees.

Multi-modal journey planning, which allows multiple types of transport within a single trip, is becoming increasingly popular, due to a strong practical interest and an increasing availability of data. In real life, transport networks feature uncertainty. Yet, most approaches assume a deterministic environment, making plans more prone to failures such as missed connections and major delays in the arrival. This paper presents an approach to computing optimal contingent plans in multi-modal journey planning. The problem is modeled as a search in an and/or state space. We describe search enhancements used on top of the AO* algorithm. Enhancements include admissible heuristics, multiple types of pruning that preserve the completeness and the optimality, and a hybrid search approach with a deterministic and a nondeterministic search. We demonstrate an NP-hardness result, with the hardness stemming from the dynamically changing distributions of the travel time random variables. We perform a detailed empirical analysis on realistic transport networks from cities such as Montpellier, Rome and Dublin. The results demonstrate the effectiveness of our algorithmic contributions, and the benefits of contingent plans as compared to standard sequential plans, when the arrival and departure times of buses are characterized by uncertainty.

This paper presents a data-driven method for transit network design that relies on a large sample of user location data available from mobile phone telecommunication networks. Such data provide opportunistic sensing and the means for transit operators to match supply with mobility demand inferred from mobile phone locations. In contrast to previous methods of transit network design, the proposed method is entirely data driven, leveraging the large-sample properties of disaggregate mobile phone network data and mobility pattern mining. The method works by deriving frequent patterns of movements from anonymized mobile phone location data and merging them to generate candidate route designs. Additional routines for optimal route selection and service frequency setting are then employed to select a network configuration made up of routes that maximizes systemwide traveler utility. Using data from half a million mobile phone users in Abidjan from the telco operator Orange, we demonstrated to provide resource-neutral system improvement of 27% in terms of end-user journey times.

The deep penetration of mobile phones offers cities the ability to opportunistically monitor citizens' mobility and use data-driven insights to better plan and manage services. With large scale data on mobility patterns, operators can move away from the costly, mostly survey based, transportation planning processes, to a more data-centric view, that places the instrumented user at the center of development. In this framework, using mobile phone data to perform transit analysis and optimization represents a new frontier with significant societal impact, especially in developing countries. In this paper we present AllAboard, an intelligent tool that analyses cellphone data to help city authorities in visually exploring urban mobility and optimizing public transport. This is performed within a self contained tool, as opposed to the current solutions which rely on a combination of several distinct tools for analysis, reporting, optimisation and planning. An interactive user interface allows transit operators to visually explore the travel demand in both space and time, correlate it with the transit network, and evaluate the quality of service that a transit network provides to the citizens at very fine grain. Operators can visually test scenarios for transit network improvements, and compare the expected impact on the travellers' experience. The system has been tested using real telecommunication data for the city of Abidjan, Ivory Coast, and evaluated from a data mining, optimisation and user prospective.

Real-time traffic awareness applications are playing an ever increasing role understanding and tackling traffic congestion in cities. First-hand accounts from drivers witnessing an incident is an invaluable source of information for traffic managers. Nowadays, drivers increasingly contact control rooms through social media to report on journey times, accidents or road weather conditions. These new interactions allow traffic controllers to engage users, and in particular to query them for information rather than passively collecting it. Querying participants presents the challenge of which users to probe for updates about a specific situation. In order to maximise the probability of a user responding and the accuracy of the information, we propose a strategy which takes into account the engagement levels of the user, the mobility profile and the reputation of the user. We provide an analysis of a real-world user corpus of Twitter users contributing updates to LiveDrive, a Dublin based traffic radio station.

The deep penetration of mobile phones offers cities the ability to opportunistically monitor citizensfi mobility and use data-driven insights to better plan and manage services. In this context, transit operators can leverage pervasive mobile sensing to better match observed demand for travel with their service offerings. In this paper we present AllAboard, an intelligent tool that analyses cellphone data to helps city authorities in exploring urban mobility and optimizing public transport. An interactive user interface allows transit operators to explore the travel demand in both space and time, evaluate the quality of service that a transit network provides to the citizens, and test scenarios for transit network improvements. The system has been tested using real telecommunication data for the city of Abidjan, Ivory Coast, and evaluated from a data mining, optimization and user prospective.

Given a set of k networks, possibly with different sizes and no overlaps in nodes or links, how can we quickly assess similarity between them? Analogously, are there a set of social theories which, when represented by a small number of descriptive, numerical features, effectively serve as a "signature" for the network? Having such signatures will enable a wealth of graph mining and social network analysis tasks, including clustering, outlier detection, visualization, etc. We propose a novel, effective, and scalable method, called NETSIMILE, for solving the above problem. Our approach has the following desirable properties: (a) It is supported by a set of social theories. (b) It gives similarity scores that are size-invariant. (c) It is scalable, being linear on the number of links for graph signature extraction. In extensive experiments on numerous synthetic and real networks from disparate domains, NETSIMILE outperforms baseline competitors. We also demonstrate how our approach enables several mining tasks such as clustering, visualization, discontinuity detection, network transfer learning, and re-identification across networks.

Within the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire

We analyze urban mobility and public places under a new perspective: how can we feature the places in a city based on how people move among them? To answer this question we need to combine places, like points of interest, with mobility information like the trajectories of individuals moving within a city. To accomplish this, we propose a methodology based on complex network analysis: we build a network of points of interests by connecting places by the individual trajectories passing through them. From such network we compute communities finding groups places highly connected by the mobility of the individuals. We present a case study on real trajectory dataset on the city of Milan, showing a complementary view on the urban mobility that is not covered by the state-of-the art techniques on mobility analysis.

Complex networks have been receiving increasing attention by the scientific community, also due to the availability of massive network data from diverse domains. One problem studied so far in complex network analysis is Community Discovery, i.e. the detection of group of nodes densely connected, or highly related. However, one aspect of such networks has been disregarded so far: real networks are often multidimensional, i.e. many connections may reside between any two nodes, either to reflect different kinds of relationships, or to connect nodes by different values of the same type of tie. In this context, the problem of Community Discovery has to be redefined, taking into account multidimensionality. In this paper, we attempt to do so, by defining the problem in the multidimensional context, and by introducing also a new measure able to characterize the communities found. We then provide a complete framework for finding and characterizing multidimensional communities. Our experiments on real world multidimensional networks support the methodology proposed in this paper, and open the way for a new class of algorithms, aimed at capturing the multifaceted complexity of connections among nodes in a network.

Complex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens inmonodimensional network, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we develop a solid repertoire of basic concepts and analytical measures, which takes into account the general structure of multidimensional networks. We tested our framework on a real world multidimensional network, showing the validity and the meaningfulness of the measures introduced, that are able to extract important, nonrandom, information about complex phenomena.

Community Discovery in networks is the problem of detecting, for each node, its membership to one of more groups of nodes, the communities, that are densely connected, or highly interactive. We define the community discovery problem in multidimensional networks, where more than one connection may reside between any two nodes. We also introduce two measures able to characterize the communities found. Our experiments on real world multidimensional networks support the methodology proposed in this paper, and open the way for a new class of algorithms, aimed at capturing the multifaceted complexity of connections among nodes in a network.

In the past few years there has been an increasing interest in the analysis of process logs. Several proposed techniques, such as workflow mining, are aimed at automatically deriving the underlying workflow models. However, current approaches only pay little attention on an important piece of information contained in process logs: the timestamps, which are used to define a sequential ordering of the performed tasks. In this work we try to overcome these limitations by explicitly including time in the extracted knowledge, thus making the temporal information a first-class citizen of the analysis process. This makes it possible to discern between apparently identical process executions that are performed with different transition times between consecutive tasks.

Clinical databases store large amounts of information about patients and their medical conditions. Data mining techniques can extract relationships and patterns holding in this wealth of data, and thus be helpful in understanding the progression of diseases and the efficacy of the associated therapies. A typical structure of medical data is a sequence of observations of clinical parameters taken at different time moments. In this kind of contexts, the temporal dimension of data is a fundamental variable that should be taken in account in the mining process and returned as part of the extracted knowledge. Therefore, the classical and well established framework of sequential pattern mining is not enough, because it only focuses on the sequentiality of events, without extracting the typical time elapsing between two particular events. Time-annotated sequences (IAS), is a novel mining paradigm that solves this problem. Recently defined in our laboratory together with an efficient algorithm for extracting them, IAS are sequential patterns where each transition between two events is annotated with a typical transition time that is found frequent in the data. In this paper we report a real-world medical case study, in which the IAS mining paradigm is applied to clinical data regarding a set of patients in the follow-up of a liver transplantation. The aim of the data analysis is that of assessing the effectiveness of the extracorporeal photopheresis (ECP) as a therapy to prevent rejection in solid organ transplantation. For each patient, a set of biochemical variables is recorded at different time moments after the transplantation. The IAS patterns extracted show the values of interleukins and other clinical parameters at specific dates, from which it is possible for the physician to assess the effectiveness of the ECP therapy. We believe that this case study does not only show the interestingness of extracting IAS patterns in this particular context but, more ambitiously, it suggests a general methodology for clinical data mining, whenever the time dimension is an important variable of the problem in analysis.

HumanLeukocyteAntigens (HLA), also known as histocompatibility antigens, are molecules found on all nucleated cells in the body. Histocompatibility antigens help the immune system to recognize whether or not a cell is foreign to the body, hence the success of an organ transplantation is strongly connected to the HLA systems of the donor-recipient pair. Beyond this important role, the HLA system seems to influence also the clinical course of hepatic cirrhosis, both on viral and autoimmune basis. However, not only different antigens have different importance w.r.t. hepatic cirrhosis, but, to make things more complicated, other not yet well characterized factors could play an important role. It is thus important to assess the existence of associations between haplotypic settings (possibly mixed with clinical and demographical information) and hepatic cirrhosis.Algorithms developed for frequent patterns discovery can help us achieve this goal. Thanks to their ability in handling a very large number of variables and the associated exponential search space, such techniques can discover interesting haplotypic patterns beyond traditional analysis methods capabilities.