The Experts below are selected from a list of 144 Experts worldwide ranked by ideXlab platform

Katja Liimatainen - One of the best experts on this subject based on the ideXlab platform.

  • challenges of government enterprise Architecture Work stakeholders views
    Electronic Government, 2008
    Co-Authors: Hannakaisa Isomaki, Katja Liimatainen
    Abstract:

    At present, a vast transformation within government systems is executed towards electronic government. In some countries, this change is initiated as enterprise Architecture Work. This paper introduces results from an empirical study on different stakeholders' views on enterprise Architecture development within Finnish state government. The data is gathered from 21 interviews accomplished during spring 2007 among participants of the Interoperability Programme of Finnish state administration. The interviewees represent different sectors and levels of Finnish government and IT companies. On the basis of qualitative data analysis we discuss challenges of enterprise Architecture Work in the context of state government. The key conclusion is that the governance level of enterprise Architecture needs to be adequately adjusted and enforced as a tool for the development of business operations.

  • EGOV - Challenges of Government Enterprise Architecture Work --- Stakeholders' Views
    Lecture Notes in Computer Science, 1
    Co-Authors: Hannakaisa Isomaki, Katja Liimatainen
    Abstract:

    At present, a vast transformation within government systems is executed towards electronic government. In some countries, this change is initiated as enterprise Architecture Work. This paper introduces results from an empirical study on different stakeholders' views on enterprise Architecture development within Finnish state government. The data is gathered from 21 interviews accomplished during spring 2007 among participants of the Interoperability Programme of Finnish state administration. The interviewees represent different sectors and levels of Finnish government and IT companies. On the basis of qualitative data analysis we discuss challenges of enterprise Architecture Work in the context of state government. The key conclusion is that the governance level of enterprise Architecture needs to be adequately adjusted and enforced as a tool for the development of business operations.

Hannakaisa Isomaki - One of the best experts on this subject based on the ideXlab platform.

  • challenges of government enterprise Architecture Work stakeholders views
    Electronic Government, 2008
    Co-Authors: Hannakaisa Isomaki, Katja Liimatainen
    Abstract:

    At present, a vast transformation within government systems is executed towards electronic government. In some countries, this change is initiated as enterprise Architecture Work. This paper introduces results from an empirical study on different stakeholders' views on enterprise Architecture development within Finnish state government. The data is gathered from 21 interviews accomplished during spring 2007 among participants of the Interoperability Programme of Finnish state administration. The interviewees represent different sectors and levels of Finnish government and IT companies. On the basis of qualitative data analysis we discuss challenges of enterprise Architecture Work in the context of state government. The key conclusion is that the governance level of enterprise Architecture needs to be adequately adjusted and enforced as a tool for the development of business operations.

  • EGOV - Challenges of Government Enterprise Architecture Work --- Stakeholders' Views
    Lecture Notes in Computer Science, 1
    Co-Authors: Hannakaisa Isomaki, Katja Liimatainen
    Abstract:

    At present, a vast transformation within government systems is executed towards electronic government. In some countries, this change is initiated as enterprise Architecture Work. This paper introduces results from an empirical study on different stakeholders' views on enterprise Architecture development within Finnish state government. The data is gathered from 21 interviews accomplished during spring 2007 among participants of the Interoperability Programme of Finnish state administration. The interviewees represent different sectors and levels of Finnish government and IT companies. On the basis of qualitative data analysis we discuss challenges of enterprise Architecture Work in the context of state government. The key conclusion is that the governance level of enterprise Architecture needs to be adequately adjusted and enforced as a tool for the development of business operations.

Andrea Acquaviva - One of the best experts on this subject based on the ideXlab platform.

  • impact of graph partitioning on snn placement for a multi core neuromorphic Architecture Work in progress
    Compilers Architecture and Synthesis for Embedded Systems, 2018
    Co-Authors: Francesco Barchi, Gianvito Urgese, Enrico Macii, Andrea Acquaviva
    Abstract:

    In this paper, we evaluate a partitioning and placement technique for mapping concurrent applications over a globally asynchronous locally synchronous (GALS) multi-core Architecture designed for simulating a spiking neural netWork (SNN) in real-time. We designed a task placement pipeline capable of analysing the netWork of neurons and producing a placement configuration that enables a reduction of communication between computational nodes. The neuron-to-core mapping problem has been formalised as a two phases problem: Partitioning and Placement. The Partitioning phase aims at grouping together the most connected netWork components, maximising the amount of self-connections within each identified group. For this purpose we used a multilevel k-way graph partitioning strategy capable of generating netWork-partitions. The Placement phase aims at placing groups of neurons over the chip mesh minimising the communication between computational nodes. For implementing this step, we designed and evaluate the performances of three placement variants. In the results, we point out the importance of using a partitioning algorithm for the SNN graph. We were able to achieve an increase in self-connections of 19% and an improvement of the final overall post-placement synaptic elongation of 29% using the simulated annealing placement technique, compared to 22% obtained without partitioning.

  • CASES - Impact of graph partitioning on SNN placement for a multi-core neuromorphic Architecture: Work-in-progress
    2018
    Co-Authors: Francesco Barchi, Gianvito Urgese, Enrico Macii, Andrea Acquaviva
    Abstract:

    In this paper, we evaluate a partitioning and placement technique for mapping concurrent applications over a globally asynchronous locally synchronous (GALS) multi-core Architecture designed for simulating a spiking neural netWork (SNN) in real-time. We designed a task placement pipeline capable of analysing the netWork of neurons and producing a placement configuration that enables a reduction of communication between computational nodes. The neuron-to-core mapping problem has been formalised as a two phases problem: Partitioning and Placement. The Partitioning phase aims at grouping together the most connected netWork components, maximising the amount of self-connections within each identified group. For this purpose we used a multilevel k-way graph partitioning strategy capable of generating netWork-partitions. The Placement phase aims at placing groups of neurons over the chip mesh minimising the communication between computational nodes. For implementing this step, we designed and evaluate the performances of three placement variants. In the results, we point out the importance of using a partitioning algorithm for the SNN graph. We were able to achieve an increase in self-connections of 19% and an improvement of the final overall post-placement synaptic elongation of 29% using the simulated annealing placement technique, compared to 22% obtained without partitioning.

Francesco Barchi - One of the best experts on this subject based on the ideXlab platform.

  • impact of graph partitioning on snn placement for a multi core neuromorphic Architecture Work in progress
    Compilers Architecture and Synthesis for Embedded Systems, 2018
    Co-Authors: Francesco Barchi, Gianvito Urgese, Enrico Macii, Andrea Acquaviva
    Abstract:

    In this paper, we evaluate a partitioning and placement technique for mapping concurrent applications over a globally asynchronous locally synchronous (GALS) multi-core Architecture designed for simulating a spiking neural netWork (SNN) in real-time. We designed a task placement pipeline capable of analysing the netWork of neurons and producing a placement configuration that enables a reduction of communication between computational nodes. The neuron-to-core mapping problem has been formalised as a two phases problem: Partitioning and Placement. The Partitioning phase aims at grouping together the most connected netWork components, maximising the amount of self-connections within each identified group. For this purpose we used a multilevel k-way graph partitioning strategy capable of generating netWork-partitions. The Placement phase aims at placing groups of neurons over the chip mesh minimising the communication between computational nodes. For implementing this step, we designed and evaluate the performances of three placement variants. In the results, we point out the importance of using a partitioning algorithm for the SNN graph. We were able to achieve an increase in self-connections of 19% and an improvement of the final overall post-placement synaptic elongation of 29% using the simulated annealing placement technique, compared to 22% obtained without partitioning.

  • CASES - Impact of graph partitioning on SNN placement for a multi-core neuromorphic Architecture: Work-in-progress
    2018
    Co-Authors: Francesco Barchi, Gianvito Urgese, Enrico Macii, Andrea Acquaviva
    Abstract:

    In this paper, we evaluate a partitioning and placement technique for mapping concurrent applications over a globally asynchronous locally synchronous (GALS) multi-core Architecture designed for simulating a spiking neural netWork (SNN) in real-time. We designed a task placement pipeline capable of analysing the netWork of neurons and producing a placement configuration that enables a reduction of communication between computational nodes. The neuron-to-core mapping problem has been formalised as a two phases problem: Partitioning and Placement. The Partitioning phase aims at grouping together the most connected netWork components, maximising the amount of self-connections within each identified group. For this purpose we used a multilevel k-way graph partitioning strategy capable of generating netWork-partitions. The Placement phase aims at placing groups of neurons over the chip mesh minimising the communication between computational nodes. For implementing this step, we designed and evaluate the performances of three placement variants. In the results, we point out the importance of using a partitioning algorithm for the SNN graph. We were able to achieve an increase in self-connections of 19% and an improvement of the final overall post-placement synaptic elongation of 29% using the simulated annealing placement technique, compared to 22% obtained without partitioning.

Mathias Ekstedt - One of the best experts on this subject based on the ideXlab platform.

  • Automatic Design of Secure Enterprise Architecture: Work in Progress Paper
    2017 IEEE 21st International Enterprise Distributed Object Computing Workshop (EDOCW), 2017
    Co-Authors: Robert Lagerström, Pontus Johnson, Mathias Ekstedt
    Abstract:

    Architecture models mainly have three functions; 1) document, 2) analyze, and 3) improve the system under consideration. All three functions have suffered from being time-consuming and expensive, mainly due to being manual processes in need of hard to find expertise. Recent Work has however automated both the data collection and the analysis. In order for enterprise Architecture modeling to finally become free of manual labor the design function also needs to be automated. In this position paper we propose the Automatic Designer. A solution that employs machine learning techniques to realize the design of (near) optimal Architecture solutions. This particular implementation is focused on security analysis, but could easily be extended to other topics.

  • EDOC Workshops - Automatic Design of Secure Enterprise Architecture: Work in Progress Paper
    2017 IEEE 21st International Enterprise Distributed Object Computing Workshop (EDOCW), 2017
    Co-Authors: Robert Lagerström, Pontus Johnson, Mathias Ekstedt
    Abstract:

    Architecture models mainly have three functions; 1) document, 2) analyze, and 3) improve the system under consideration. All three functions have suffered from being timeconsuming and expensive, mainly due to being manual processes in need of hard to find expertise. Recent Work has however automated both the data collection and the analysis. In order for enterprise Architecture modeling to finally become free of manual labor the design function also needs to be automated. In this position paper we propose the Automatic Designer. A solution that employs machine learning techniques to realize the design of (near) optimal Architecture solutions. This particular implementation is focused on security analysis, but could easily be extended to other topics.