From big data to big performance – exploring the potential of big data for enhancing public organizations’ performance : a systematic literature review

This article examines the possibilities for increasing organizational performance in the public sector using Big Data by conducting a systematic literature review. It includes the results of 36 scientific articles published between January 2012 and July 2019. The results show a tendency to explain the relationship between big data and organizational performance through the Resource-Based View of the Firm or the Dynamic Capabilities View, arguing that perfor-mance improvement in an organization stems from unique capabilities. In addition, the results show that Big Data performance improvement is influenced by better organizational decision making. Finally, it identifies three dimensions that seem to play a role in this process: the human dimension, the organizational dimension, and the data dimension. From these findings, implications for both practice and theory are derived

Integration of Exploration and Search: A Case Study of the M3 Model

International audienceEffective support for multimedia analytics applications requires exploration and search to be integrated seamlessly into a single interaction model. Media metadata can be seen as defining a multidimensional media space, casting multimedia analytics tasks as exploration, manipulation and augmentation of that space. We present an initial case study of integrating exploration and search within this multidimensional media space. We extend the M3 model, initially proposed as a pure exploration tool, and show that it can be elegantly extended to allow searching within an exploration context and exploring within a search context. We then evaluate the suitability of relational database management systems, as representatives of today’s data management technologies, for implementing the extended M3 model. Based on our results, we finally propose some research directions for scalability of multimedia analytics

Visual analytics of flight trajectories for uncovering decision making strategies

In air traffic management and control, movement data describing actual and planned flights are used for planning, monitoring and post-operation analysis purposes with the goal of increased efficient utilization of air space capacities (in terms of delay reduction or flight efficiency), without compromising the safety of passengers and cargo, nor timeliness of flights. From flight data, it is possible to extract valuable information concerning preferences and decision making of airlines (e.g. route choice) and air traffic managers and controllers (e.g. flight rerouting or optimizing flight times), features whose understanding is intended as a key driver for bringing operational performance benefits. In this paper, we propose a suite of visual analytics techniques for supporting assessment of flight data quality and data analysis workflows centred on revealing decision making preferences

Semantics-Space-Time Cube. A Conceptual Framework for Systematic Analysis of Texts in Space and Time

We propose an approach to analyzing data in which texts are associated with spatial and temporal references with the aim to understand how the text semantics vary over space and time. To represent the semantics, we apply probabilistic topic modeling. After extracting a set of topics and representing the texts by vectors of topic weights, we aggregate the data into a data cube with the dimensions corresponding to the set of topics, the set of spatial locations (e.g., regions), and the time divided into suitable intervals according to the scale of the planned analysis. Each cube cell corresponds to a combination (topic, location, time interval) and contains aggregate measures characterizing the subset of the texts concerning this topic and having the spatial and temporal references within these location and interval. Based on this structure, we systematically describe the space of analysis tasks on exploring the interrelationships among the three heterogeneous information facets, semantics, space, and time. We introduce the operations of projecting and slicing the cube, which are used to decompose complex tasks into simpler subtasks. We then present a design of a visual analytics system intended to support these subtasks. To reduce the complexity of the user interface, we apply the principles of structural, visual, and operational uniformity while respecting the specific properties of each facet. The aggregated data are represented in three parallel views corresponding to the three facets and providing different complementary perspectives on the data. The views have similar look-and-feel to the extent allowed by the facet specifics. Uniform interactive operations applicable to any view support establishing links between the facets. The uniformity principle is also applied in supporting the projecting and slicing operations on the data cube. We evaluate the feasibility and utility of the approach by applying it in two analysis scenarios using geolocated social media data for studying people's reactions to social and natural events of different spatial and temporal scales

Innovating for Learning: Designing for the Future of Education

Teaching has moved online as the world has moved online and learning is losing its sense of physical location with the availability of many different options from mobile to MOOC (Massive Open Online Course). The impact of online learning is not confined to distance learning; when a student attends a campus university they are now as likely to meet with their fellow learners virtually as face to face. The education sector has yet to fully adapt to what this means, and indeed there strong signs of a built in resilience from providers, employers and students themselves which may mean an apparent evolution is more likely than a revolution. At the same time, there are some quiet changes underway that mean we should be preparing to innovate for the revolution to come. Some of those changes are considered in work undertaken at The Open University that has been disseminated in a series of Innovating Pedagogy reports. These reports allow the academic authors to be more speculative than is usual practice and engage in considering the future, while remaining based on a view of what is happening in the sector. In particular they adopt a position focused on pedagogy that balances technology-based futurology that can dominate yet fail to resonate with those actually involved in the teaching process. The annual Innovating Pedagogy reports cover 10 topics each, with some deliberate overlap from year to year and development of themes that show innovations moving into teaching practice. This is illustrated by two cases, the impact of MOOCs and the application of learning design and analytics. The development of MOOCs demonstrates the value of reviewing pedagogy that aligns with technology. While the use of learning design and learning analytics demonstrates how improvements in the way we describe our learning processes and the way we understand learner behaviour is helping determine how choices in pedagogy impact on student satisfaction, progression and success

Query-driven learning for predictive analytics of data subspace cardinality

Fundamental to many predictive analytics tasks is the ability to estimate the cardinality (number of data items) of multi-dimensional data subspaces, defined by query selections over datasets. This is crucial for data analysts dealing with, e.g., interactive data subspace explorations, data subspace visualizations, and in query processing optimization. However, in many modern data systems, predictive analytics may be (i) too costly money-wise, e.g., in clouds, (ii) unreliable, e.g., in modern Big Data query engines, where accurate statistics are difficult to obtain/maintain, or (iii) infeasible, e.g., for privacy issues. We contribute a novel, query-driven, function estimation model of analyst-defined data subspace cardinality. The proposed estimation model is highly accurate in terms of prediction and accommodating the well-known selection queries: multi-dimensional range and distance-nearest neighbors (radius) queries. Our function estimation model: (i) quantizes the vectorial query space, by learning the analysts’ access patterns over a data space, (ii) associates query vectors with their corresponding cardinalities of the analyst-defined data subspaces, (iii) abstracts and employs query vectorial similarity to predict the cardinality of an unseen/unexplored data subspace, and (iv) identifies and adapts to possible changes of the query subspaces based on the theory of optimal stopping. The proposed model is decentralized, facilitating the scaling-out of such predictive analytics queries. The research significance of the model lies in that (i) it is an attractive solution when data-driven statistical techniques are undesirable or infeasible, (ii) it offers a scale-out, decentralized training solution, (iii) it is applicable to different selection query types, and (iv) it offers a performance that is superior to that of data-driven approaches

Is One Hyperparameter Optimizer Enough?

Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, there has not been much discussion on which hyperparameter tuner is best for software analytics. To address this gap in the literature, this paper applied a range of hyperparameter optimizers (grid search, random search, differential evolution, and Bayesian optimization) to defect prediction problem. Surprisingly, no hyperparameter optimizer was observed to be `best' and, for one of the two evaluation measures studied here (F-measure), hyperparameter optimization, in 50\% cases, was no better than using default configurations. We conclude that hyperparameter optimization is more nuanced than previously believed. While such optimization can certainly lead to large improvements in the performance of classifiers used in software analytics, it remains to be seen which specific optimizers should be applied to a new dataset.Comment: 7 pages, 2 columns, accepted for SWAN1