1,721,010 research outputs found
Solving open polygonals in elastic correction of dead-reckoning errors
No full textA major problem in map building is due to the imprecision of sensor measures. In a previous paper we proposed a technique, called elastic correction, for correcting the dead-reckoning errors made during the exploration of an unknown environment by a robot capable of identifying landmarks. Elastic correction is based on an analogy between the relational graph modelling the environment and a mechanical structure: the map is regarded as a truss where each route is an elastic bar and each landmark a node; errors are corrected as a result of the deformations induced from the forces arising within the structure as inconsistent measures are taken. The main weakness of this method lies in the way positional inconsistencies are solved when routes are covered for the first time. In this paper we improve first-sight elastic correction by replacing the heuristics previously adopted with a new approach which considers all the knowledge of the surrounding map acquired so far; this is achieved by calculating the minimum forces to be applied in order to restore metric consistency. The effectiveness of the new approach is demonstrated by presenting some experimental tests
Towards a Process-Driven Design of Data Platforms
Full text linkData platforms are state-of-the-art solutions to implement data-driven applications and analytics, since they facilitate the ingestion, storage, management, and exploitation of big data. Data platforms are built on top of complex ecosystems of services answering different data needs and requirements; such ecosystems are offered by different providers (e.g., Amazon AWS and Apache). However, when it comes to engineering data platforms, no unifying strategy and methodology is there yet, and the design is mainly left to the expertise of practitioners in the field. In particular, service providers simply expose a long list of interoperable and alternative engines, making it hard to select the optimal subset without a deep knowledge of the ecosystem. A more effective approach to the design starts from the knowledge of the data transformation and exploitation processes that should be supported by the platform. In this paper, we sketch a computer-aided design methodology and then focus on the selection of the optimal services needed to implement such processes. We believe that our approach lightens the design of data platforms and enables an unbiased selection and comparison of solutions even through different service ecosystems
Conversational OLAP
Full text linkThe 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 paper, we describe COOL, a framework devised for COnversational OLap applications. COOL interprets and translates a natural language dialog into an OLAP session that starts with a GPSJ (Generalized Projection, Selection, and Join) query and continues with the application of OLAP operators. The interpretation relies on a formal grammar and on a repository storing metadata and values from a multidimensional cube. In case of ambiguous text description, COOL can obtain the correct query either through automatic inference or user interactions to disambiguate the text
Towards conversational OLAP
Full text linkThe 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 paper, we envisage a conversational framework specifically devised for OLAP applications. The system converts natural language text in GPSJ (Generalized Projection, Selection and Join) queries. The approach relies on an ad-hoc grammar and a knowledge base storing multidimensional metadata and cubes values. In case of ambiguous or incomplete query description, the system is able to obtain the correct query either through automatic inference or through interactions with the user to disambiguate the text. Our tests show very promising results both in terms of effectiveness and efficiency
COOL: A framework for conversational OLAP
Full text linkThe 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 paper, we introduce COOL, a framework devised for COnversational OLap applications. COOL interprets and translates a natural language dialog into an OLAP session that starts with a GPSJ (Generalized Projection, Selection, and Join) query and continues with the application of OLAP operators. The interpretation relies on a formal grammar and on a repository storing metadata and values from a multidimensional cube. In case of ambiguous or incomplete text description, COOL can obtain the correct query either through automatic inference or user interactions to disambiguate the text. Our tests show very promising results in terms of effectiveness, efficiency, and user experience. Besides adding novel support to the interpretation and translation of complete analytical OLAP sessions, COOL achieves an average accuracy of 94% in the interpretation of GPSJ queries from real datasets
Multi-sensor profiling for precision soil-moisture monitoring
Full text linkControlling soil moisture is crucial in optimizing watering and crop performance. Traditional monitoring systems rely on a single sensor or on a column of sensors that do not allow farmers to properly capture soil moisture dynamics in the soil volume occupied by roots. In this paper we propose PLUTO, an original approach that builds fine-grained 2D and 3D soil moisture profiles by relying on a grid of sensors. Profiles are computed using both interpolation-based and machine learning approaches. Besides the technical description of the approach, the paper reports a set of original visualizations and a large set of tests computed, over two years, on real Kiwi orchards. PLUTO proved to largely overcome the accuracy of profiles obtained with traditional sensor layouts. Considering that the cost of sensors is progressively decreasing, PLUTO provides a cost-effective, operative, and precise solution to moisture monitoring
Correction of dead-reckoning errors in map building for mobile robots
No full textMap building is an important issue for all the applications in mobile robotics in which the environment is unknown and, in general, in order to have a robot exhibit a fully autonomous behavior. A major problem in map building is due to the imprecision of sensor measures. In this paper, we propose a technique, called elastic correction, for correcting the dead-reckoning errors made during the exploration of an environment by a robot capable of identifying landmarks. Knowledge being acquired is modeled by a relational graph whose vertices and arcs represent, respectively, landmarks and routes. Elastic correction is based on an analogy between the graph modeling the environment and a mechanical structure: the map is regarded as a truss where each route is an elastic bar and each landmark a node. Errors are corrected as a result of the deformations induced from the forces arising within the structure as inconsistent measures are taken. The elasticity parameters characterizing the structures are used to model the uncertainty on odometry. The paper presents results from simulations showing the effectiveness of the method for reducing the overall metric error and proving its robustness with reference to topological errors and to unpredictable sensor errors
Colossal Trajectory Mining: A unifying approach to mine behavioral mobility patterns
Full text linkSpatio-temporal mobility patterns are at the core of strategic applications such as urban planning and monitoring. Depending on the strength of spatio-temporal constraints, different mobility patterns can be defined. While existing approaches work well in the extraction of groups of objects sharing fine-grained paths, the huge volume of large-scale data asks for coarse-grained solutions. In this paper, we introduce Colossal Trajectory Mining (CTM) to efficiently extract heterogeneous mobility patterns out of a multidimensional space that, along with space and time dimensions, can consider additional trajectory features (e.g., means of transport or activity) to characterize behavioral mobility patterns. The algorithm is natively designed in a distributed fashion, and the experimental evaluation shows its scalability with respect to the involved features and the cardinality of the trajectory dataset
Fine-grained Soil Moisture Monitoring with PLUTO
Full text linkControlling soil moisture is crucial in optimizing watering and crop performance, particularly for crops
with high water demands such as Kiwi. Monitoring and simulating soil behavior are two key approaches
to understand soil behavior. Proximal sensors are the most reliable way to monitor soil moisture. While
in the past sensor costs limited their adoption, the progressive cost reduction makes now possible to
properly capture moisture dynamics in the soil volume occupied by roots. Physically-based numerical
models can be used to further understand soil moisture dynamics, but solely in an off-line manner due to
their time-consuming simulations. We introduce PLUTO, a cost-effective solution that, starting from
sensor data, leverages both Physically-based and machine learning models to build on-line moisture
profiles for long-term watering optimization. PLUTO, relies on bi/tri dimensional sensor grids that
proved to largely overcome the accuracy of previous profiles obtained with traditional sensor layouts.
Besides, we provide an analysis of sensor importance that takes in consideration the trade-off between
accuracy, number, and position in order to suggest a smart placement
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