Fabio Mercorio

A universal planning system for hybrid domains

Many real world problems involve hybrid systems, subject to (continuous) physical effects and controlled by (discrete) digital equipments. Indeed, many efforts are being made to extend the current planning systems and modelling languages to support such kind of domains. However, hybrid systems often present also a nonlinear behaviour and planning with continuous nonlinear change that is still a challenging issue.In this paper we present the UPMurphi tool, a universal planner based on the discretise and validate approach that is capable of reasoning with mixed discrete/continuous domains, fully respecting the semantics of PDDL+. Given an initial discretisation, the hybrid system is discretised and given as input to UPMurphi, which performs universal planning on such an approximated model and checks the correctness of the results. If the validation fails, the approach is repeated by appropriately refining the discretisation.To show the effectiveness of our approach, the paper presents two real hybrid domains where universal planning has been successfully performed using the UPMurphi tool.

A model-based evaluation of data quality activities in KDD

Abstract We live in the Information Age, where most of the personal, business, and administrative data are collected and managed electronically. However, poor data quality may affect the effectiveness of knowledge discovery processes, thus making the development of the data improvement steps a significant concern. In this paper we propose the Multidimensional Robust Data Quality Analysis, a domain-independent technique aimed to improve data quality by evaluating the effectiveness of a black-box cleansing function. Here, the proposed approach has been realized through model checking techniques and then applied on a weakly structured dataset describing the working careers of millions of people. Our experimental outcomes show the effectiveness of our model-based approach for data quality as they provide a fine-grained analysis of both the source dataset and the cleansing procedures, enabling domain experts to identify the most relevant quality issues as well as the action points for improving the cleansing activities. Finally, an anonymized version of the dataset and the analysis results have been made publicly available to the community.

Automatic Synthesis of Data Cleansing Activities

Data cleansing is growing in importance among both public and private organisations, mainly due to the relevant amount of data exploited for supporting decision making processes. This paper is aimed to show how model-based verification algorithms (namely, model checking) can contribute in addressing data cleansing issues, furthermore a new benchmark problem focusing on the labour market dynamic is introduced. The consistent evolution of the data is checked using a model defined on the basis of domain knowledge. Then, we formally introduce the concept of universal cleanser, i.e. an object which summarises the set of all cleansing actions for each feasible data inconsistency (according to a given consistency model), then providing an algorithm which synthesises it. The universal cleanser can be seen as a repository of corrective interventions useful to develop cleansing routines. We applied our approach to a dataset derived from the Italian labour market data, making the whole dataset and outcomes publicly available to the community, so that the results we present can be shared and compared with other techniques.

Data Quality Sensitivity Analysis on Aggregate Indicators

Decision making activities stress data and information quality requirements. The quality of data sources is frequently very poor, therefore a cleansing process is required before using such data for decision making processes. When alternative (and more trusted) data sources are not available data can be cleansed only using business rules derived from domain knowledge. Business rules focus on fixing inconsistencies, but an inconsistency can be cleansed in different ways (i.e. the correction can be not deterministic), therefore the choice on how to cleanse data can (even strongly) affect the aggregate values computed for decision making purposes. The paper proposes a methodology exploiting Finite State Systems to quantitatively estimate how computed variables and indicators might be affected by the uncertainty related to low data quality, independently from the data cleansing methodology used. The methodology has been implemented and tested on a real case scenario providing effective results.

Inconsistency Knowledge Discovery for Longitudinal Data Management: A Model-Based Approach

In the last years, the growing diffusion of IT-based services has given a rise to the use of huge masses of data. However, using data for analytical and decision making purposes requires to perform several tasks, e.g. data cleansing, data filtering, data aggregation and synthesis, etc. Tools and methodologies empowering people are required to appropriately manage the (high) complexity of large datasets.

Model checking for universal planning in deterministic and non-deterministic domains

This thesis is concerned with solving planning problems in deterministic and non-deterministic domains through explicit model checking techniques. The contribution is twofold: on one side we developed the V-UPMurphi tool, which applies a disk-based planning approach for efficient storage during the plan synthesis. On the other side we developed and formally analysed an algorithm to solve cost-optimal strong planning problems in non-deterministic contexts. Finally, we applied the developed algorithms and tools to several application domains, many of which are inspired by real world scenarios.

A Policy-Based Cleansing and Integration Framework for Labour and Healthcare Data

Large amounts of data are collected by public administrations and healthcare organizations, the integration of the data scattered in several information systems can facilitate the comprehension of complex scenarios and support the activities of decision makers.

Data quality through model checking techniques

The paper introduces the Robust Data Quality Analysis which exploits formal methods to support Data Quality Improvement Processes. The proposed methodology can be applied to data sources containing sequences of events that can be modelled by Finite State Systems. Consistency rules (derived from domain business rules) can be expressed by formal methods and can be automatically verified on data, both before and after the execution of cleansing activities. The assessment results can provide useful information to improve the data quality processes. The paper outlines the preliminary results of the methodology applied to a real case scenario: the cleansing of a very low quality database, containing the work careers of the inhabitants of an Italian province. The methodology has proved successful, by giving insights on the data quality levels and by providing suggestions on how to ameliorate the overall data quality process.

Challenge: Processing web texts for classifying job offers

Today the Web represents a rich source of labour market data for both public and private operators, as a growing number of job offers are advertised through Web portals and services. In this paper we apply and compare several techniques, namely explicit-rules, machine learning, and LDA-based algorithms to classify a real dataset of Web job offers collected from 12 heterogeneous sources against a standard classification system of occupations.

A Model-Based Approach for Developing Data Cleansing Solutions

The data extracted from electronic archives is a valuable asset; however, the issue of the (poor) data quality should be addressed before performing data analysis and decision-making activities. Poor data quality is frequently cleansed using business rules derived from domain knowledge. Unfortunately, the process of designing and implementing cleansing activities based on business rules requires a relevant effort. In this article, we illustrate a model-based approach useful to perform inconsistency identification and corrective interventions, thus simplifying the process of developing cleansing activities. The article shows how the cleansing activities required to perform a sensitivity analysis can be easily developed using the proposed model-based approach. The sensitivity analysis provides insights on how the cleansing activities can affect the results of indicators computation. The approach has been successfully used on a database describing the working histories of an Italian area population. A model formalizing how data should evolve over time (i.e., a data consistency model) in such domain was created (by means of formal methods) and used to perform the cleansing and sensitivity analysis activities.