The Experts below are selected from a list of 186 Experts worldwide ranked by ideXlab platform

George Loizou - One of the best experts on this subject based on the ideXlab platform.

  • why is the Snowflake Schema a good data warehouse design
    Information Systems, 2003
    Co-Authors: Mark Levene, George Loizou
    Abstract:

    Database design for data warehouses is based on the notion of the Snowflake Schema and its important special case, the star Schema. The Snowflake Schema represents a dimensional model which is composed of a central fact table and a set of constituent dimension tables which can be further broken up into subdimension tables. We formalise the concept of a Snowflake Schema in terms of an acyclic database Schema whose join tree satisfies certain structural properties. We then define a normal form for Snowflake Schemas which captures its intuitive meaning with respect to a set of functional and inclusion dependencies. We show that Snowflake Schemas in this normal form are independent as well as separable when the relation Schemas are pairwise incomparable. This implies that relations in the data warehouse can be updated independently of each other as long as referential integrity is maintained. In addition, we show that a data warehouse in Snowflake normal form can be queried by joining the relation over the fact table with the relations over its dimension and subdimension tables. We also examine an information-theoretic interpretation of the Snowflake Schema and show that the redundancy of the primary key of the fact table is zero.

  • Why is the Snowflake Schema a good data warehouse design
    2003
    Co-Authors: Mark Levene, George Loizou
    Abstract:

    Database design for data warehouses is based on the notion of the Snowflake Schema and its important special case, the star Schema. The Snowflake Schema represents a dimensional model which is composed of a central fact table and a set of constituent dimension tables which can be further broken up into subdimension tables. We formalise the concept of a Snowflake Schema in terms of an acyclic database Schema whose join tree satisfies certain structural properties. We then define a normal form for Snowflake Schemas which captures its intuitive meaning with respect to a set of functional and inclusion dependencies. We show that Snowflake Schemas in this normal form are independent as well as separable when the relation Schemas are pairwise incomparable. This implies that relations in the data warehouse can be updated independently of each other as long as referential integrity is maintained. In addition, we show that a data warehouse in Snowflake normal form can be queried by joining the relation over the fact table with the relations over its dimension and subdimension tables. We also examine an information-theoretic interpretation of the Snowflake Schema and show that the redundancy of the primary key of the fact table is zero. Key words. Data warehouse design, star and Snowflake Schema, independent and separable database Schema, acyclic database Schema.

  • Why is the Star Schema a Good Data Warehouse Design?
    1999
    Co-Authors: Mark Levene, George Loizou
    Abstract:

    Database design for data warehouses is based on the notion of the Snowflake Schema and its important special case, the star Schema. The Snowflake Schema represents a dimensional model which is composed of a central fact table and a set of constituent dimension tables which can be further broken up into subdimension tables. We formalise the concept of a Snowflake Schema in terms of an acyclic database Schema whose join tree satisfies certain structural properties. We then define a normal form for Snowflake Schemas which captures its intuitive meaning with respect to a set of functional and inclusion dependencies. We show that Snowflake Schemas in this normal form are independent as well as separable when the relation Schemas are pairwise incomparable. This implies that relations in the data warehouse can be updated independently of each other as long as referential integrity is maintained. In addition, we show that a data warehouse in Snowflake normal form can be queried by joining the relation over the fact table with the relations over its dimension and subdimension tables. We also examine an information-theoretic interpretation of the Snowflake Schema and show that the redundancy of the primary key of the fact table is zero

Mark Levene - One of the best experts on this subject based on the ideXlab platform.

  • why is the Snowflake Schema a good data warehouse design
    Information Systems, 2003
    Co-Authors: Mark Levene, George Loizou
    Abstract:

    Database design for data warehouses is based on the notion of the Snowflake Schema and its important special case, the star Schema. The Snowflake Schema represents a dimensional model which is composed of a central fact table and a set of constituent dimension tables which can be further broken up into subdimension tables. We formalise the concept of a Snowflake Schema in terms of an acyclic database Schema whose join tree satisfies certain structural properties. We then define a normal form for Snowflake Schemas which captures its intuitive meaning with respect to a set of functional and inclusion dependencies. We show that Snowflake Schemas in this normal form are independent as well as separable when the relation Schemas are pairwise incomparable. This implies that relations in the data warehouse can be updated independently of each other as long as referential integrity is maintained. In addition, we show that a data warehouse in Snowflake normal form can be queried by joining the relation over the fact table with the relations over its dimension and subdimension tables. We also examine an information-theoretic interpretation of the Snowflake Schema and show that the redundancy of the primary key of the fact table is zero.

  • Why is the Snowflake Schema a good data warehouse design
    2003
    Co-Authors: Mark Levene, George Loizou
    Abstract:

    Database design for data warehouses is based on the notion of the Snowflake Schema and its important special case, the star Schema. The Snowflake Schema represents a dimensional model which is composed of a central fact table and a set of constituent dimension tables which can be further broken up into subdimension tables. We formalise the concept of a Snowflake Schema in terms of an acyclic database Schema whose join tree satisfies certain structural properties. We then define a normal form for Snowflake Schemas which captures its intuitive meaning with respect to a set of functional and inclusion dependencies. We show that Snowflake Schemas in this normal form are independent as well as separable when the relation Schemas are pairwise incomparable. This implies that relations in the data warehouse can be updated independently of each other as long as referential integrity is maintained. In addition, we show that a data warehouse in Snowflake normal form can be queried by joining the relation over the fact table with the relations over its dimension and subdimension tables. We also examine an information-theoretic interpretation of the Snowflake Schema and show that the redundancy of the primary key of the fact table is zero. Key words. Data warehouse design, star and Snowflake Schema, independent and separable database Schema, acyclic database Schema.

  • Why is the Star Schema a Good Data Warehouse Design?
    1999
    Co-Authors: Mark Levene, George Loizou
    Abstract:

    Database design for data warehouses is based on the notion of the Snowflake Schema and its important special case, the star Schema. The Snowflake Schema represents a dimensional model which is composed of a central fact table and a set of constituent dimension tables which can be further broken up into subdimension tables. We formalise the concept of a Snowflake Schema in terms of an acyclic database Schema whose join tree satisfies certain structural properties. We then define a normal form for Snowflake Schemas which captures its intuitive meaning with respect to a set of functional and inclusion dependencies. We show that Snowflake Schemas in this normal form are independent as well as separable when the relation Schemas are pairwise incomparable. This implies that relations in the data warehouse can be updated independently of each other as long as referential integrity is maintained. In addition, we show that a data warehouse in Snowflake normal form can be queried by joining the relation over the fact table with the relations over its dimension and subdimension tables. We also examine an information-theoretic interpretation of the Snowflake Schema and show that the redundancy of the primary key of the fact table is zero

Dian Eka Ratnawati - One of the best experts on this subject based on the ideXlab platform.

  • pengembangan data warehouse dan online analytical processing olap untuk data artikel pada jurnal teknologi informasi dan ilmu komputer jtiik
    Jurnal Sistem Informasi Teknologi Informasi dan Edukasi Sistem Informasi, 2020
    Co-Authors: Bella Krisanda Easterita, Issa Arwani, Dian Eka Ratnawati
    Abstract:

    Saat ini, Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) telah terakreditasi dengan peringkat 2. Dengan JTIIK yang telah terakreditasi, maka peminat peneliti untuk mengirimkan artikel ke JTIIK semakin tinggi. Namun, proses untuk menerbitkan suatu artikel memerlukan waktu lebih dari satu tahun. Agar proses penerbitan tidak memerlukan waktu yang terlalu lama, mulai pada tahun 2018 JTIIK menerbitkan jurnal sebanyak enam kali dimana penerbitan mengalami peningkatan dari tahun sebelumnya. Dari peningkatan penerbitan ini, data yang disimpan juga akan semakin bertambah. Sebuah sistem diperlukan untuk mengelola data yang besar dari beberapa sumber dan melakukan analisis yang dapat menjadi bahan pertimbangan dalam pengambilan keputusan. Pada penelitian ini dilakukan pengembangan data warehouse dan Online Analytical Processing (OLAP) sebagai langkah penyelesaian untuk masalah dalam mengelola dan menganalisis banyaknya data dari beberapa sumber. Dalam penelitian ini dilakukan analisis sebagai tahapan pertama. Berdasarkan hasil analisis didapatkan 2 information package yaitu information package data artikel dan information package data penulis. Penelitian dilanjutkan dengan perancangan data warehouse dengan menggunakan Snowflake Schema yang menghasilkan 2 tabel fakta dan 4 tabel dimensi. Selanjutnya dilakukan implementasi ETL dan OLAP. Kemudian dilakukan 2 jenis pengujian. Hasil pengujian pertama yaitu validasi kebutuhan menunjukkan bahwa data warehouse yang dibangun telah sesuai dengan kebutuhan yang dirancang. Hasil pengujian kedua yaitu performansi proses ETL dari segi waktu menunjukkan bahwa hasil rata-rata waktu yang dibutuhkan untuk eksekusi proses ETL adalah 14,5 detik.

Easterita, Bella Krisanda - One of the best experts on this subject based on the ideXlab platform.

  • Pengembangan Data Warehouse dan Online Analytical Processing (OLAP) untuk Analisis Data Artikel pada Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
    2020
    Co-Authors: Easterita, Bella Krisanda
    Abstract:

    Saat ini, Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) telah terakreditasi dengan peringkat 2. Dengan JTIIK yang telah terakreditasi, maka peminat peneliti untuk mengirimkan artikel ke JTIIK semakin tinggi. Namun, proses untuk menerbitkan suatu artikel memerlukan waktu lebih dari satu tahun. Agar proses penerbitan tidak memerlukan waktu yang terlalu lama, mulai pada tahun 2018 JTIIK menerbitkan jurnal sebanyak enam kali dimana penerbitan mengalami peningkatan dari tahun sebelumnya. Dari peningkatan penerbitan ini, data yang disimpan juga akan semakin bertambah. Sebuah sistem diperlukan untuk mengelola data yang besar dari beberapa sumber dan melakukan analisis yang dapat menjadi bahan pertimbangan dalam pengambilan keputusan. Pada penelitian ini dilakukan pengembangan data warehouse dan Online Analytical Processing (OLAP) sebagai langkah penyelesaian untuk masalah dalam mengelola dan menganalisis banyaknya data dari beberapa sumber. Dalam penelitian ini dilakukan analisis sebagai tahapan pertama. Berdasarkan hasil analisis didapatkan 2 information package yaitu information package data artikel dan information package data penulis. Penelitian dilanjutkan dengan perancangan data warehouse dengan menggunakan Snowflake Schema yang menghasilkan 2 tabel fakta dan 4 tabel dimensi. Selanjutnya dilakukan implementasi ETL dan OLAP. Kemudian dilakukan 2 jenis pengujian. Hasil pengujian pertama yaitu validasi kebutuhan menunjukkan bahwa data warehouse yang dibangun telah sesuai dengan kebutuhan yang dirancang. Hasil pengujian kedua yaitu performansi proses ETL dari segi waktu menunjukkan bahwa hasil rata-rata waktu yang dibutuhkan untuk eksekusi proses ETL adalah 14,5 detik

Bella Krisanda Easterita - One of the best experts on this subject based on the ideXlab platform.

  • pengembangan data warehouse dan online analytical processing olap untuk data artikel pada jurnal teknologi informasi dan ilmu komputer jtiik
    Jurnal Sistem Informasi Teknologi Informasi dan Edukasi Sistem Informasi, 2020
    Co-Authors: Bella Krisanda Easterita, Issa Arwani, Dian Eka Ratnawati
    Abstract:

    Saat ini, Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) telah terakreditasi dengan peringkat 2. Dengan JTIIK yang telah terakreditasi, maka peminat peneliti untuk mengirimkan artikel ke JTIIK semakin tinggi. Namun, proses untuk menerbitkan suatu artikel memerlukan waktu lebih dari satu tahun. Agar proses penerbitan tidak memerlukan waktu yang terlalu lama, mulai pada tahun 2018 JTIIK menerbitkan jurnal sebanyak enam kali dimana penerbitan mengalami peningkatan dari tahun sebelumnya. Dari peningkatan penerbitan ini, data yang disimpan juga akan semakin bertambah. Sebuah sistem diperlukan untuk mengelola data yang besar dari beberapa sumber dan melakukan analisis yang dapat menjadi bahan pertimbangan dalam pengambilan keputusan. Pada penelitian ini dilakukan pengembangan data warehouse dan Online Analytical Processing (OLAP) sebagai langkah penyelesaian untuk masalah dalam mengelola dan menganalisis banyaknya data dari beberapa sumber. Dalam penelitian ini dilakukan analisis sebagai tahapan pertama. Berdasarkan hasil analisis didapatkan 2 information package yaitu information package data artikel dan information package data penulis. Penelitian dilanjutkan dengan perancangan data warehouse dengan menggunakan Snowflake Schema yang menghasilkan 2 tabel fakta dan 4 tabel dimensi. Selanjutnya dilakukan implementasi ETL dan OLAP. Kemudian dilakukan 2 jenis pengujian. Hasil pengujian pertama yaitu validasi kebutuhan menunjukkan bahwa data warehouse yang dibangun telah sesuai dengan kebutuhan yang dirancang. Hasil pengujian kedua yaitu performansi proses ETL dari segi waktu menunjukkan bahwa hasil rata-rata waktu yang dibutuhkan untuk eksekusi proses ETL adalah 14,5 detik.