Describing and Assessing Cubes Through Intentional Analytics

The Intentional Analytics Model (IAM) has been envisioned as a way to tightly couple OLAP and analytics by (i) letting users explore multidimensional cubes stating their intentions, and (ii) returning multidimensional data coupled with knowledge insights in the form of annotations of subsets of data. Goal of this demonstration is to showcase the IAM approach using a notebook where the user can create a data exploration session by writing describe and assess statements, whose results are displayed by combining tabular data and charts so as to bring the highlights discovered to the user's attention. The demonstration plan will show the effectiveness of the IAM approach in supporting data exploration and analysis and its added value as compared to a traditional OLAP session by proposing two scenarios with guided interaction and letting users run custom sessions

Conversational OLAP in Action

The democratization of data access and the adoption of OLAP in scenarios requiring hand-free interfaces push towards the creation of smart OLAP interfaces. In this demonstration we present COOL, a tool supporting natural language COnversational OLap sessions. COOL interprets and translates a natural language dialogue into an OLAP session that starts with a GPSJ (Generalized Projection, Selection and Join) query. The interpretation relies on a formal grammar and a knowledge base storing metadata from a multidimensional cube. COOL is portable, robust, and requires minimal user intervention. It adopts an n-gram based model and a string similarity function to match known entities in the natural language description. In case of incomplete text description, COOL can obtain the correct query either through automatic inference or through interactions with the user to disambiguate the text. The goal of the demonstration is to let the audience evaluate the usability of COOL and its capabilities in assisting query formulation and ambiguity/error resolution

Crop Management with the IoT: an Interdisciplinary Survey

In this study we analyze how crop management is going to benefit from the Internet of Things providing an overview of its architecture and components from an agronomic and a technological perspective. The present analysis highlights that IoT is a mature enabling technology, with articulated hardware and software components. Cheap networked devices may sense crop fields at a finer grain, to give timeliness warnings on stress conditions and the presence of disease to a wider range of farmers. Cloud computing allows to reliably store and access heterogeneous data, developing and deploy farm services. From this study emerges that IoT is also going to increase attention to sensor quality and placement protocol, while Machine Learning should be oriented to produce understandable knowledge, which is also useful to enhance Cropping System Simulation systems

Graduates’ employment and employability after the “Bologna Process” reform. Evidence from the Italian experience and methodological issues

In a phase of depression and systemic crisis investments are essential assets in organizing the recovery, and the more so when innovation is relevant. This is why universities, companies, households and graduates implement strategies for overcoming the present crisis, leading to structural changes and competition both at the local and international level. In this framework, tracer studies on graduates transition to the labour markets provides fundamental insights and information not only to the organizations responsible for their training, but also to the economic system as a whole. Moreover, any such study is all the more useful when it can draw upon reliable and up-to-date information. This paper emphasizes three main points. First we present the results achieved by the AL model in tracing the transition path of graduates from the time they enrolled at the university until a few years after earning the degree. The survey is carried out every year by the AL and makes it possible to analyze the most recent labour market trends through the scrutiny of the career opportunities available for the graduates after 1, 3 and 5 years on from graduation. More specifically, we will present the results of the 2008 survey. This survey involved also all first and second level graduates from the 2007 vintage. Second, we examine the revision in our survey method, adopted in order to face the need to monitor a much higher number of post-reform graduates (more than 140 thousand overall) and the call of the Ministry and the universities to keep the information as much detailed as possible in assessing the employment outcomes for each single degree course, without losing feasibility in terms of costs and data collection time. In fact, we resorted to a mixed method: the computer assisted web interviewing (CAWI) and the computer assisted telephone interviewing (CATI). This is why it became necessary to measure and assess the effect of this approach on the answers given by interviewed graduates. In third place, we outline the results of some preliminary experiments carried on in order to allow for specific and recurrent comparisons between the results achieved with the AL model and other similar models dealing with the employment conditions of Italian graduates.Graduates’ employment; Graduates’ employability; Bologna Process; University reform; University governance; Assessment of the higher education system; CAWI and CATI survey techniques; Propensity score matching; Data quality control; Counter factual analysis; Labour supply, Human capital.

Multi-phase and Multi-component CFD Analysis of a Load - Sensing Proportional Control Valve

The paper analyzes the flow through a directional control valve for load –sensing application by means of a multi-phase and multi-component CFD approach. Numerical modeling includes both cavitation and aeration; in particular, the Rayleigh-Plesset equation and the inertia controlled growth model for bubble formation are adopted. The effects of gas release and vapor formation as well as turbulence on the main valve metering characteristics are investigated. The results show a remarkable influence of the aeration phenomena on the recirculating zones downstream of the metering area and thus on the cavitation onset region

Energy recovery of the biomass from livestock farms in Italy: the case of Modena Province

The energy recovery from manure of different Italian livestock farms is analysed by means of numerical simulation using an in-house developed code. In particular, the animal farming in the province of Modena is taken into account and biomass is exploited in an integrated system including different waste to energy technologies. In the considered system, the manure of a number of types of animals is fed into an anaerobic digester, while the digested sludge is separated into the solid and liquid fractions. The former is employed as a fuel in a downdraft gasifier, while the latter is purified by means of both forward and inverse osmosis. Finally, the obtained bio-gas and syngas are used in a cogeneration system based on a spark ignition internal combustion engine to produce electric and thermal power. The potential power production of the considered territory is estimated and compared with the energy requirements of the animal farms. Different strategies for the distributed exploitation of the manure versus a centralized solution are investigated and the relating plant size and production of electric energy and thermal energy are evaluated

Rationalising the difference in crystallisability of two sulflowers using efficient in silico methods

The molecular structures of the first and second generation sulflowers, sulflower and persulfurated coronene (PSC), are remarkably similar: carbon ring structures decorated with sulfur atoms, without any additional moiety

Trajectory-Based Spatiotemporal Entity Linking

Trajectory-based spatiotemporal entity linking is to match the same moving object in different datasets based on their movement traces. It is a fundamental step to support spatiotemporal data integration and analysis. In this paper, we study the problem of spatiotemporal entity linking using effective and concise signatures extracted from their trajectories. This linking problem is formalized as a k-nearest neighbor (k-NN) query on the signatures. Four representation strategies (sequential, temporal, spatial, and spatiotemporal) and two quantitative criteria (commonality and unicity) are investigated for signature construction. A simple yet effective dimension reduction strategy is developed together with a novel indexing structure called the WR-tree to speed up the search. A number of optimization methods are proposed to improve the accuracy and robustness of the linking. Our extensive experiments on real-world datasets verify the superiority of our approach over the state-of-the-art solutions in terms of both accuracy and efficiency.Comment: 15 pages, 3 figures, 15 table

In Silico Identification of MYB and bHLH Families Reveals Candidate Transcription Factors for Secondary Metabolic Pathways in Cannabis sativa L

Plant secondary metabolic pathways are finely regulated by the activity of transcription factors, among which members of the bHLH and MYB subfamilies play a main role. Cannabis sativa L. is a unique officinal plant species with over 600 synthesized phytochemicals having diverse scale-up industrial and pharmaceutical usage. Despite comprehensive knowledge of cannabinoids\u2019 metabolic pathways, very little is known about their regulation, while the literature on flavonoids\u2019 metabolic pathways is still scarce. In this study, we provide the first genome-wide analysis of bHLH and MYB families in C. sativa reference cultivar CBDRx and identification of candidate coding sequences for these transcription factors. Cannabis sativa bHLHs and MYBs were then classified into functional subfamilies through comparative phylogenetic analysis with A. thaliana transcription factors. Analyses of gene structure and motif distribution confirmed that CsbHLHs and CsMYBs belonging to the same evolutionary clade share common features at both gene and amino acidic level. Candidate regulatory genes for key metabolic pathways leading to flavonoid and cannabinoid synthesis in Cannabis were also retrieved. Furthermore, a candidate gene approach was used to identify structural enzyme-coding genes for flavonoid and cannabinoid synthesis. Taken as a whole, this work represents a valuable resource of candidate genes for further investigation of the C. sativa cannabinoid and flavonoid metabolic pathways for genomic studies and breeding programs