From e30f6b813d05d47bf2976a46559d2ba291e88b1a Mon Sep 17 00:00:00 2001 From: Tristan Nolde Date: Thu, 25 Jan 2024 17:49:23 +0100 Subject: [PATCH] docs/document-structure (#578) Habe per toctree die verschiedenen Dokumente in die richtige Reihenfolge gebracht. --------- Co-authored-by: Tim Ronneburg Co-authored-by: Sebastian <94404394+SeZett@users.noreply.github.com> Co-authored-by: Philipp Horstenkamp Co-authored-by: SeZett --- documentations/Ergebnisse/1_Einleitung.md | 4 + documentations/Ergebnisse/2_Recherche.md | 4 + documentations/Ergebnisse/3_Grundlagen.md | 11 + documentations/Ergebnisse/4_Umsetzung.md | 9 + .../SaZh/S1.md | 6 + .../SeZe/Abbildungen/Teamrollen.PNG | Bin 0 -> 2684899 bytes .../SeZe/S5-1.md | 75 + .../TiRo/S2.md | 25 +- .../TiRo/S3.md | 2 +- .../TiRo/S4-5-2.md | 12 +- .../TrNo/S4-2.md | 2 +- .../TrNo/S4-3-1.md | 2 +- .../SeZe}/00_Datenspeicherung.md | 112 +- .../SeZe}/00_Datenspeicherung.pdf | Bin .../SeZe}/2023-06-21_Datenspeicherung.pptx | Bin .../Jupyter/01_Stammdaten_Unternehmen_HR.csv | 0 .../Jupyter/01_Stammdaten_Unternehmen_HR2.csv | 0 .../11_Create_Tables_with_SQL-Alchemy.ipynb | 0 .../SeZe}/Jupyter/Amtsgerichte.csv | 0 .../SeZe}/Jupyter/BASF_Data.csv | 0 .../SeZe}/Jupyter/EON_Data.csv | 0 .../01_Connect_to_Database.ipynb | 0 .../01_Stammdaten_Unternehmen.csv | 0 ...ect_to_Database_publish_Company_Data.ipynb | 0 .../03-1_Publish_Finance_Testdata_BASF.ipynb | 0 ...3-2_Publish-Finance_Testdata_Telekom.ipynb | 0 .../03-2_Publish_Finance_Testdata_EON.ipynb | 0 .../04_First_Query.ipynb | 0 .../Amtsgerichte.csv | 0 .../BASF_Data_NewOrder.csv | 0 .../EON_Data_NewOrder.csv | 0 .../Telekom_Data_NewOrder.csv | 0 .../person.csv | 0 .../person2.csv | 0 .../SeZe}/Jupyter/Telekom_Data.csv | 0 .../SeZe}/Jupyter/edges.csv | 0 .../SeZe}/Jupyter/person1000.csv | 0 .../SeZe}/images/DB_Schema.PNG | Bin .../SeZe}/images/Data_Cluster.PNG | Bin .../SeZe}/images/Document_DB.PNG | Bin .../SeZe}/images/Front.PNG | Bin .../SeZe}/images/Graph.PNG | Bin .../SeZe}/images/HLD.PNG | Bin .../SeZe}/images/PG_Admin_Board.PNG | Bin .../SeZe}/images/PG_Admin_Conf.PNG | Bin .../SeZe}/images/Staging_DB.PNG | Bin .../SeZe}/images/company.PNG | Bin .../SeZe}/images/finance_data.PNG | Bin .../TiRo/verflechtungsanalyse.md | 22 +- documentations/index.rst | 30 +- .../project_management/Arch.-Planning.drawio | 71 +- poetry.lock | 15797 ++++++++-------- 52 files changed, 8140 insertions(+), 8044 deletions(-) create mode 100644 documentations/Ergebnisse/1_Einleitung.md create mode 100644 documentations/Ergebnisse/2_Recherche.md create mode 100644 documentations/Ergebnisse/3_Grundlagen.md create mode 100644 documentations/Ergebnisse/4_Umsetzung.md create mode 100644 documentations/Ergebnisse/Abschlussbericht_und_Praesentation/SaZh/S1.md create mode 100644 documentations/Ergebnisse/Abschlussbericht_und_Praesentation/SeZe/Abbildungen/Teamrollen.PNG create mode 100644 documentations/Ergebnisse/Abschlussbericht_und_Praesentation/SeZe/S5-1.md rename 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b/documentations/Ergebnisse/1_Einleitung.md new file mode 100644 index 0000000..73e5396 --- /dev/null +++ b/documentations/Ergebnisse/1_Einleitung.md @@ -0,0 +1,4 @@ +# Einleitung +```{toctree} +Abschlussbericht_und_Praesentation/SaZh/S1.md +``` diff --git a/documentations/Ergebnisse/2_Recherche.md b/documentations/Ergebnisse/2_Recherche.md new file mode 100644 index 0000000..0a8a698 --- /dev/null +++ b/documentations/Ergebnisse/2_Recherche.md @@ -0,0 +1,4 @@ +# Recherche +```{toctree} +Abschlussbericht_und_Praesentation/TiRo/S2.md +``` diff --git a/documentations/Ergebnisse/3_Grundlagen.md b/documentations/Ergebnisse/3_Grundlagen.md new file mode 100644 index 0000000..fb0126b --- /dev/null +++ b/documentations/Ergebnisse/3_Grundlagen.md @@ -0,0 +1,11 @@ +# Grundlagen und Basistechniken (Tim Ronneburg) +In diesem Abschnitt sind die Zwischenberichte des Projekts zu finden. In diesen Berichten wurde die Recherche zu den einzelnen Kategorien durchgeführt und in den entsprechenden Jupyter Notebooks im Git-Repository bewertet. Dieses Kapitel präsentiert eine detaillierte Analyse der zuvor beschriebenen Recherche mit zusätzlichen Begründungen und Erläuterungen, warum bestimmte Technologien, Konzepte und Vorgehensweisen für das Projekt ausgewählt wurden. + +Es ist zu beachten, dass die Zwischenberichte zu einem frühen Zeitpunkt des Projektes erstellt wurden und eine Hausarbeit darstellen, weshalb in einigen Abschnitten von zukünftigen Vorgehensweisen und Ausblick gesprochen wird. + +```{toctree} +Zwischenbericht_und_Praesentation/PhHo/dev-ops +Zwischenbericht_und_Praesentation/TrNo/Ausarbeitung.md +Zwischenbericht_und_Praesentation/SeZe/00_Datenspeicherung.md +Zwischenbericht_und_Praesentation/TiRo/verflechtungsanalyse.md +``` diff --git a/documentations/Ergebnisse/4_Umsetzung.md b/documentations/Ergebnisse/4_Umsetzung.md new file mode 100644 index 0000000..7adde97 --- /dev/null +++ b/documentations/Ergebnisse/4_Umsetzung.md @@ -0,0 +1,9 @@ +# Technische Realisation +```{toctree} +Abschlussbericht_und_Praesentation/PhHo/05-DEV-OPS +Abschlussbericht_und_Praesentation/TrNo/S4-2.md +Abschlussbericht_und_Praesentation/TrNo/S4-3-1.md +Abschlussbericht_und_Praesentation/PhHo/4-4-2-database-generator +Abschlussbericht_und_Praesentation/TiRo/S4-5-2.md +Abschlussbericht_und_Praesentation/PhHo/04-data-visualisation-container.md +``` diff --git a/documentations/Ergebnisse/Abschlussbericht_und_Praesentation/SaZh/S1.md b/documentations/Ergebnisse/Abschlussbericht_und_Praesentation/SaZh/S1.md new file mode 100644 index 0000000..f922e40 --- /dev/null +++ b/documentations/Ergebnisse/Abschlussbericht_und_Praesentation/SaZh/S1.md @@ -0,0 +1,6 @@ +## Zielsetzung + +*TO BE CONTINUED* + +## Projektorganisation +Ein toller Platzhalter diff --git a/documentations/Ergebnisse/Abschlussbericht_und_Praesentation/SeZe/Abbildungen/Teamrollen.PNG b/documentations/Ergebnisse/Abschlussbericht_und_Praesentation/SeZe/Abbildungen/Teamrollen.PNG new file mode 100644 index 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- # Datenspeicherung -## Inhaltsverzeichnis - -- [Datenspeicherung](#datenspeicherung) - - [Inhaltsverzeichnis](#inhaltsverzeichnis) - - [Motivation: Warum speichern wird Daten?](#motivation-warum-speichern-wird-daten) - - [1. Allgemeine Anforderungen an Datenbank](#1-allgemeine-anforderungen-an-datenbank) - - [2. Datenarten](#2-datenarten) - - [2.1 Welche Daten erwarten wir im Projekt?](#21-welche-daten-erwarten-wir-im-projekt) - - [2.2 strukturierte Daten](#22-strukturierte-daten) - - [2.3 unstrukturierte Daten](#23-unstrukturierte-daten) - - [3. Arten von Datenbanken](#3-arten-von-datenbanken) - - [3.1 Relationale Datenbank](#31-relationale-datenbank) - - [3.1.1 Anlegen von Tabellen](#311-anlegen-von-tabellen) - - [3.1.2 SQL - Abfrage von relationalen Datenbanken](#312-sql---abfrage-von-relationalen-datenbanken) - - [3.2 Graphdatenbank](#32-graphdatenbank) - - [3.2.1 Erstellung eines Datensatzes](#321-erstellung-eines-datensatzes) - - [3.2.2 Cypher - Abfrage von Graphdatenbanken](#322-cypher---abfrage-von-graphdatenbanken) - - [3.3 Zeitseriendatenbank](#33-zeitseriendatenbank) - - [3.3.1 Erstellung eines Datensatzes](#331-erstellung-eines-datensatzes) - - [3.3.2 FluxQuery](#332-fluxquery) - - [3.4 Dokumenten Datenbank ](#34-dokumenten-datenbank-) - - [3.4.1 Erstellen einer Collection / Ablegen von Dokumenten](#341-erstellen-einer-collection--ablegen-von-dokumenten) - - [3.5 Aufbau einer Datenbank](#35-aufbau-einer-datenbank) - - [4. Datenbanken Transparenzregister](#4-datenbanken-transparenzregister) - - [4.1 Production DB - relationales Datenbankmodell](#41-production-db---relationales-datenbankmodell) - - [4.2 Staging DB](#42-staging-db) - - [4.3 SQL Alchemy](#43-sql-alchemy) - - [5. Proof of Concept](#5-proof-of-concept) - - [5.1 Docker](#51-docker) - - [5.2 PG Admin](#52-pg-admin) - - [5.3 Erstellen von Mock Daten](#53-erstellen-von-mock-daten) - - [5.4 Anlegen der relationalen Tabellen](#54-anlegen-der-relationalen-tabellen) - - [5.5 Abfragen der Datenbank](#55-abfragen-der-datenbank) - - [6. Zusammenfassung](#6-zusammenfassung) - - [Quellen](#quellen) - -
- ## Motivation: Warum speichern wird Daten? Für die Speicherung von Daten gibt es verschiedene Motivationen: @@ -59,9 +19,9 @@ Grundlage für Analysen darstellen. \ Mit geeigneten Pipelines werden aus diesen Daten Erkenntnisse extrahiert, um z.B. Verflechtungen zwischen Personen und Unternehmen oder den wirtschaftlichen Trend eines Unternehmens visualisieren und bewerten zu können. -## 1. Allgemeine Anforderungen an Datenbank +## Allgemeine Anforderungen an Datenbank -- **1.1 Speicherung/Integrität**: Das verwendete System muss Daten, wie Unternehmenskennzahlen, Stammdaten und +- **Speicherung/Integrität**: Das verwendete System muss Daten, wie Unternehmenskennzahlen, Stammdaten und Verflechtungen speichern. Die Daten müssen korrekt und konsistent gespeichert werden. Konsistent bedeutet in einem gültigen und widerspruchsfreien Zustand und die Transaktionen sollen den ACID-Eigenschaften entsprechen. - **Atomarity:** Eine Transaktion wird atomar betrachte, d.h. es ist die kleinste unteilbare Einheit, wodurch eine @@ -76,11 +36,11 @@ Unternehmen oder den wirtschaftlichen Trend eines Unternehmens visualisieren und diese bei der Ausführung nicht gegenseitig beeinflussen. - **Durability:** Dauerhaftigkeit bedeutet, dass die Ergebnisse einer Transaktion dauerhaft in der Datenbank gespeichert werden und auch nach einem Systemneustart oder Systemfehler erhalten bleiben. -- **1.2 Skalierbarkeit:** Das System soll skalierbar sein, um zukünftige Daten weiterhin zu speichern und weitere +- **Skalierbarkeit:** Das System soll skalierbar sein, um zukünftige Daten weiterhin zu speichern und weitere Unternehmen hinzuzufügen. Durch Hinzufügen von Ressourcen kann das System an steigende Datenmengen und Benutzeranforderungen angepasst werden. Man spricht von horizontaler Skalierung, da die Last auf mehrere Datenbankserver verteilt wird. -- **1.3 Sicherheit:** Die Datenbank muss Mechanismen bereitstellen, um die Daten vor unbefugtem Zugriff zu schützen. +- **Sicherheit:** Die Datenbank muss Mechanismen bereitstellen, um die Daten vor unbefugtem Zugriff zu schützen. - **Authentifizierung:** Überprüfung der Identität eines Benutzers, durch Benutzername und Passwort. Meist wird eine Zwei-Faktor-Authentifizierung verwendet, um das Sicherheitslevel zu erhöhen. - **Autorisierung:** Der authentifizierte Benutzer erhält bei der Autorisierung Zugriffsrechte und Ressourcen, @@ -92,7 +52,7 @@ Unternehmen oder den wirtschaftlichen Trend eines Unternehmens visualisieren und Inhalt vor unbefugtem Zugriff zu schützen. Dafür wird ein Algorithmus verwendet, welcher einen Schlüssel generiert und die Daten mit diesem verschlüsselt. Um die Daten wieder lesen zu können, müssen diese mit dem Schlüssel dechiffriert werden. -- **1.4 Datensicherung- und Wiederherstellung:** Die Datenbank muss Funktionen zur Sicherung und Wiederherstellung +- **Datensicherung- und Wiederherstellung:** Die Datenbank muss Funktionen zur Sicherung und Wiederherstellung unterstützen. Im Falle eines Ausfalls oder Fehlers muss sichergestellt sein, dass Mechanismen die Daten schützen und wiederherstellen. Die meisten Daten in einer Datenbank ändern sich nur langsam, manche allerdings schnell. Je nach Anwendungsfall muss @@ -134,7 +94,7 @@ entwerfen. \ VDSL 100:$\frac {500GB}{\frac{5MB/s}{1024}} = 102400 sec. \approx 1706Min. \approx 28,4 Std.$ \ Glasfaser:$\frac {500GB}{\frac{62,5MB/s}{1024}} = 8192 sec. \approx 136,5Min. \approx 2,3 Std.$ -- **1.5 Leistung:** Die Performanceanforderungen an die Datenbank ergibt sich aus verschiedenen Merkmalen. Diese können +- **Leistung:** Die Performanceanforderungen an die Datenbank ergibt sich aus verschiedenen Merkmalen. Diese können kombiniert gestellt werden und sind abhängig von den Anforderungen an das System. Eine Analyse der Anwendungsfälle ist notwendig, um die Anforderungen zu spezifizieren. - **Latenz:** Die Datenbank soll Anfragen effizient und in einer akzeptablen Antwortzeit verarbeiten. Typische @@ -150,7 +110,7 @@ entwerfen. \ - **Wartbarkeit:** Eine einfach zu wartende Datenbank muss Funktionen zur Überwachung, Diagnose, Wartung, Datensicherung und Wiederherstellung bereitstellen. Durch diese automatisierten Pipelines können andere Eigenschaften, wie z.B. die Verfügbarkeit negativ beeinflusst werden, weil Prozesse die Datenbank blockieren. -- **1.6 Integration:** Die Datenbank muss Schnittstellen bereitstellen, um die gespeicherten Daten für eine Anwendung +- **Integration:** Die Datenbank muss Schnittstellen bereitstellen, um die gespeicherten Daten für eine Anwendung bzw. Systeme zur Verfügung zu stellen. - **API:** Das *Application Programming Interface* ist eine definierte Schnittstelle, welche Methoden und Funktionen bereit stellt, um auf die Datenbank zuzugreifen bzw. um diese zu verwalten. @@ -163,7 +123,7 @@ entwerfen. \ Datenbanken. - **JDBC:** Java Database Connectivity -## 2. Datenarten +## Datenarten Zur Beschreibung von Unternehmen, werden verschiedene Datenarten verwendet. Die folgenden Datenarten sind eine allgemeine Zusammenfassung und sollen das Brainstorming für die projektspezifischen @@ -197,7 +157,7 @@ Diese Datenarten müssen im Kontext des Projektes betrachtet werden und sollen d *Referenzdaten:* Einheit von Metriken (Umsatz, EBIT usw.) \ *Bestandsdaten:* Vorstand, Geschäftsführer, Aufsichtsrat -### 2.1 Welche Daten erwarten wir im Projekt? +### Welche Daten erwarten wir im Projekt? Aus den vorangehenden, allgemeinen Datenarten haben wir Cluster identifiziert, welche im Projekt benötigt werden. Die Kombination aus den folgend aufgeführten Datenclustern ermöglicht eine ganzheitliche Betrachtung und Bewertung der @@ -241,7 +201,7 @@ Momentaufnahme, bevor das relationale Schema entwickelt und die Implementierung ![Data_Clusters](images/Data_Cluster.PNG) -### 2.2 strukturierte Daten +### Strukturierte Daten Strukturierte Daten liegen in einem definierten Format. Vorab wird ein Schema definiert, um Felder, Datentypen und Reihenfolgen festzulegen und die Daten entsprechend abzulegen. @@ -271,7 +231,7 @@ classDiagram | bei bekannten Schema sind Werkzeuge vorhanden | begrenze Speichermöglichkeit, da starre Schemata vorgegeben sind | | gut automatisierbar | | -### 2.3 unstrukturierte Daten +### Unstrukturierte Daten Unstrukturierte Daten unterliegen keinem Schema, wie z.B. E-Mails, Textdokumente, Blogs, Chats, Bilder, Videos oder Audiodateien. @@ -293,9 +253,9 @@ die Entscheidungsfindung bei unklaren Sachverhalten und die Entdeckung neuer Erk | unbegrenzte Anwendungsmöglichkeiten, da kein Schema vorhanden ist | spezielle Tools zur Aufbereitung notwendig | | | Expertenwissen über die Daten und Datenaufbereitung notwendig | -## 3. Arten von Datenbanken +## Arten von Datenbanken -### 3.1 Relationale Datenbank +### Relationale Datenbank Eine relationale Datenbank speichert und verwaltet strukturierte Daten. Dabei werden die Daten in Tabellen organisiert, welche aus Zeilen und Spalten bestehen. \ @@ -344,7 +304,7 @@ Es gibt verschiedene Beziehungstypen: https://www.ibm.com/docs/de/control-desk/7.6.1.2?topic=structure-database-relationships -#### 3.1.1 Anlegen von Tabellen +#### Anlegen von Tabellen Der Umgang von relationalen Datenbanken erfolgt mittels SQL. Folgend ein Beispiel zum Anlegen einer Tabelle mit Attributen. @@ -359,7 +319,7 @@ CREATE TABLE Bildungsstaette ); ``` -#### 3.1.2 SQL - Abfrage von relationalen Datenbanken +#### SQL - Abfrage von relationalen Datenbanken Für die Verwaltung und Abfrage wird SQL (Structured Query Language) verwendet. Mit dieser Syntax können Tabellen erstellt, Daten eingefügt, aktualisiert und gelöscht und Daten abgefragt werden. @@ -394,7 +354,7 @@ FROM table1 t1 JOIN table2 t2 ON t1.id = t2.id; ``` -### 3.2 Graphdatenbank +### Graphdatenbank Eine Graphdatenbank basiert auf dem Graphenkonzept. \ Ein Graph besteht aus Knoten und Kanten (Beziehungen), welche die Verbindungen zwischen den Knoten darstellen. \ @@ -413,7 +373,7 @@ beachtet werden, wohingegen eine ungerichtete Kante eine symmetrische Beziehung **Label:** Label werden verwendet, um die Knoten zu kategorisieren/gruppieren. Ein Knoten kann auch mehrere Label besitzen, um die Zugehörigkeit an verschiedenen Kategorien darzustellen (z.B. Unternehmensbranche). -#### 3.2.1 Erstellung eines Datensatzes +#### Erstellung eines Datensatzes 1. Knotenerstellung: Es wird zuerst ein Knoten erstellt, der die Entität repräsentiert. 2. ID: Der Knoten benötigt eine eindeutige Identifikationsnummer, welche automatisch erzeugt oder manuell festgelegt @@ -438,7 +398,7 @@ CREATE ![Graph_example](images/Graph.PNG) -#### 3.2.2 Cypher - Abfrage von Graphdatenbanken +#### Cypher - Abfrage von Graphdatenbanken Um Daten abzufragen wird die Abfragesprache Cypher verwendet. Es werden folgend nur einige grundlegende Befehle gezeigt. @@ -473,7 +433,7 @@ WHERE n1.property = value1 AND n2.property = value2 RETURN r ``` -### 3.3 Zeitseriendatenbank +### Zeitseriendatenbank Zeitserien fallen überall dort an, wo eine Metrik zeitlich betrachtet wird, wie z.B. Umsatz oder EBIT. D.h. zu jedem Messwert gibt es einen zeitlich zugeordneten Zeitstempel, wobei die einzelnen Zeitpunkte zu einer Serie @@ -494,7 +454,7 @@ zusammen, welche erforderlich oder optional sind: | Fields | Min. 1 Field=value Paar wird benötigt
Nicht alle Felder müssen in jedem Punkt vorhanden sein
Datentypen: Float, String, Integer, Boolean | | Timestamp | Sind optional
Influx schreibt standardmäßig die Systemzeit als Zeitstempel
Genauigkeit kann eingestellt werden (Default: Nanosekunden) | -#### 3.3.1 Erstellung eines Datensatzes +#### Erstellung eines Datensatzes Die Einrichtung von Zeitseriendatenbanken erfolgt mit der CLI von Influx. @@ -504,7 +464,7 @@ Die Einrichtung von Zeitseriendatenbanken erfolgt mit der CLI von Influx. CREATE DATABASE finance ``` -#### 3.3.2 FluxQuery +#### FluxQuery Zur Abfrage von Datenpunkten gibt es FluxQuery, welche sich stark an SQL orientiert. \ @@ -532,7 +492,7 @@ filter(fn: (r) => r._measurement == "temperature") map(fn: (r) => ({r with temperatureF: r.temperature * 2.34 + 123})) ``` -### 3.4 Dokumenten Datenbank +### Dokumenten Datenbank Eine Dokumentendatenbank ist ein System, welches für das Speichern von Dokumenten entwicklet wurde. Es gibt verschiedene Arten von Dokumenten, wie z.B. Textdateien (JSON, HTML, XML) oder PDF. @@ -552,7 +512,7 @@ kann z.B. ein JSON mit einer eigenen internen Struktur. ![Document_DB](images/Document_DB.PNG) -#### 3.4.1 Erstellen einer Collection / Ablegen von Dokumenten +#### Erstellen einer Collection / Ablegen von Dokumenten Folgend ein Code-Snippet zum Verbinden mit der Datenbank, Anlegen einer Collection und ablegen von Dokumenten. @@ -590,7 +550,7 @@ client.close() ``` -### 3.5 Aufbau einer Datenbank +### Aufbau einer Datenbank Vor dem Aufbau einer relationalen Datenbank sollten planerische Schritte durchgeführt werden, um ein System zu entwerfen, dass den Anforderungen gerecht wird. \ @@ -612,7 +572,7 @@ erstellt werden. Es werden die zuvor festgelegten Bezeichner, Datenytpen und Con **Beziehungen festlegen:** Um die Beziehungen zwischen Tabellen festzulegen, werden Fremdschlüssel verwendet. Mit Fremdschlüsseln verknüpft man Tabellen mit den Primärschlüsseln anderer, abhängiger Tabellen. -## 4. Datenbanken Transparenzregister +## Datenbanken Transparenzregister Nachdem die Datencluster identifiziert wurden, welche für das Transparenzregister notwendig sind, wurde Rechereche zu den benötigten Datenquellen betrieben. \ @@ -628,7 +588,7 @@ Mit unterschiedlichen Daten-Extraktionspipelines (Dazta Loader, Sentiment Analys der Staging DB verarbeitet und die strukturierten und aufbereiteten Daten in der Production DB abgelegt. \ Das Frontend kann auf diese strukturierten Daten zugreifen, um diese zu visualisieren. -### 4.1 Production DB - relationales Datenbankmodell +### Production DB - relationales Datenbankmodell Für die Production DB ist eine relationale Datenbank vorgesehen, da diese die Daten organisiert und durch Verwendung von definierten Schemata strukturiert. \ @@ -663,7 +623,7 @@ Abschließend gibt es noch die Tabelle **company_relation**, welche die Verbindu Hierfür wurde ein Enumaration-Typ erzeugt, welcher die Art der Beziehung angibt (wird_beliefert_von, arbeitet_mit, ist_beteiligt_an, hat_Anteile_an). -### 4.2 Staging DB +### Staging DB Die Staging DB ist eine dokumentbasierte Datenbank zu Speicherung von unstrukturierten und semi-strukturierten Daten. Sie dient als Zwischenspeicher oder "Rohdatenbank" für die Extraktions-Pipelines. @@ -681,7 +641,7 @@ Die Staging DB erhält Collections der unterschiedlichen Quellen, unter welchen ![Staging_DB](images/Staging_DB.PNG) -### 4.3 SQL Alchemy +### SQL Alchemy SQL Alchemy ist eine Python Bibliothek, um mit relationalen Datenbanken zu kommunizieren. Dieses ORM (Object-Relational-Mapping) Framework bildet die Datenbanktabellen als Pythonklassen an und vereinfacht damit @@ -695,9 +655,9 @@ wichtige Eigenschaften: Plattformunabhängigkeit) - Erhöhung der Produktivität: Es werden keine Kompetenzen für SQL Programierung und Wartung benötigt. -## 5. Proof of Concept +## Proof of Concept -### 5.1 Docker +### Docker Für die Umsetzung der bisher vorgestellten theoretischen Betrachtungen wird ein Docker Container verwendet. Dieser Container beinhaltet eine relationale und eine dokumentbasierte Datenbank. \ @@ -761,7 +721,7 @@ Zum Starten des Containers den folgenden Befehl ausführen: docker-compose -f docker-compose.yml up ``` -### 5.2 PG Admin +### PG Admin PG Admin ist ein grafisches Administartionstool für Postgres. Wenn der Container gestartet ist, kann man sich über http://localhost:5050/browser/ mit dem Web-UI verbinden. \ @@ -772,7 +732,7 @@ Die Anmeldedaten lauten: ``` > User: admin@fh-swf.de > Passwort: admin -```` +``` ![PGAdmin_landing](images/PG_Admin_Board.PNG) @@ -788,7 +748,7 @@ geöffneten Dialog muss die Konfiguration festgelegt werden. ![PGAdmin_Conf](images/PG_Admin_Conf.PNG) -### 5.3 Erstellen von Mock Daten +### Erstellen von Mock Daten **Unternehmensstammdaten:** @@ -816,7 +776,7 @@ generiert. **Unternehmens-Unternehmens-Beziehung:** keine Mock-Daten vorhanden -### 5.4 Anlegen der relationalen Tabellen +### Anlegen der relationalen Tabellen Für das Verbinden zu der Postgre Datenbank und das Anlegen der Tabellen wird ein Jupyter Notebooks verwendet ( 11_Create_Tables_with_SQL-Alchemy.ipynb). @@ -884,7 +844,7 @@ Das grundsätzliche Vorgehen bei der Verwendung von SQLAlchemy ist: session.commit() ``` -### 5.5 Abfragen der Datenbank +### Abfragen der Datenbank Das folgende Code-Snippet zeigt, wie man eine Abfrage gestaltet. @@ -940,7 +900,7 @@ for comp in Comps:
-## 6. Zusammenfassung +## Zusammenfassung Die vorliegende Seminararbeit behandelt das Thema der Datenspeicherung mit Fokus auf dem Projekt Transparenzregister. Es wurde erläutert, warum Daten gespeichert werden und welche Art von Daten es gibt. diff --git a/documentations/seminararbeiten/Datenspeicherung/00_Datenspeicherung.pdf b/documentations/Ergebnisse/Zwischenbericht_und_Praesentation/SeZe/00_Datenspeicherung.pdf similarity index 100% rename from documentations/seminararbeiten/Datenspeicherung/00_Datenspeicherung.pdf rename to documentations/Ergebnisse/Zwischenbericht_und_Praesentation/SeZe/00_Datenspeicherung.pdf diff --git a/documentations/seminararbeiten/Datenspeicherung/2023-06-21_Datenspeicherung.pptx b/documentations/Ergebnisse/Zwischenbericht_und_Praesentation/SeZe/2023-06-21_Datenspeicherung.pptx similarity index 100% rename from documentations/seminararbeiten/Datenspeicherung/2023-06-21_Datenspeicherung.pptx rename to documentations/Ergebnisse/Zwischenbericht_und_Praesentation/SeZe/2023-06-21_Datenspeicherung.pptx diff --git a/documentations/seminararbeiten/Datenspeicherung/Jupyter/01_Stammdaten_Unternehmen_HR.csv 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a/documentations/Ergebnisse/Zwischenbericht_und_Praesentation/TiRo/verflechtungsanalyse.md b/documentations/Ergebnisse/Zwischenbericht_und_Praesentation/TiRo/verflechtungsanalyse.md index 63173d0..191b574 100644 --- a/documentations/Ergebnisse/Zwischenbericht_und_Praesentation/TiRo/verflechtungsanalyse.md +++ b/documentations/Ergebnisse/Zwischenbericht_und_Praesentation/TiRo/verflechtungsanalyse.md @@ -24,7 +24,7 @@ Werken anderer Autoren entnommen sind, habe ich als solche kenntlich gemacht. Die Arbeit wurde bisher weder gesamt noch in Teilen einer anderen Prüfungsbehörde vorgelegt und auch noch nicht veröffentlicht. -2023-10-01 +2023-10-01 image @@ -413,7 +413,7 @@ Für die Erstellung der Daten wird mit der Python Bibliothek Pandas aus einer Excel Datei Mockup-Daten zu verschiedenen Automobilherstellern geladen. -``` {.python language="Python" breaklines="true"} +```python # import pandas import pandas as pd @@ -429,7 +429,7 @@ node_color = { 'Automobilhersteller': ' #729b79ff', 'Automobilzulieferer': '#475b63ff', 'Branche 3': '#f3e8eeff', - 'Branche 4': '#bacdb0ff', + 'Branche 4': '#bacdb0ff', 'Branche 5': '#2e2c2fff', } df_nodes['color'] = df_nodes['branche'].map(node_color) @@ -447,11 +447,11 @@ print(df_nodes.head()) Als Ergebnis erhält man ein Dataframe mit den verschiedenen Automobilherstellern. -| **ID** | **Name** | **Typ** | -|--------|---------------------------|---------| -| 1 | Porsche Automobil Holding | Company | -| 2 | Volkswagen AG | Company | -| 3 | Volkswagen | Company | +| **ID** | **Name** | **Typ** | +|--------|----------|---------| +| 1 | Porsche Automobil Holding | Company | +| 2 | Volkswagen AG | Company | +| 3 | Volkswagen | Company | *Tabelle 1: Tabelle der Automobilhersteller.* @@ -463,7 +463,7 @@ erstellt, aus welchem mit der Bibliothek NetworkX ein Graph erstellt wird. Dafür wird die Methode from_pandas_edgelist genutzt - diese erstellt aus einem Dataframe einen Graphen. -``` {.python language="Python" breaklines="true"} +```python # import networkx import networkx as nx @@ -539,7 +539,7 @@ Netzwerks zugegriffen werden. Diese wird zu Nutze gemacht, um in einer for-Schleife die Größe der Ecken neu zu setzen. Der Quellcodes sieht wie folgt aus: -``` {.python language="Python" breaklines="true"} +```python adj_list = net.get_adj_list() measure_vector = {} @@ -840,4 +840,4 @@ Springer Fachmedien Wiesbaden, 2020, S. 269–300, isbn: 9783658265236. - **[7]** A. Disney,https://cambridge-intelligence.com/keylines-faqs-social-network-analysis/. -- **[8]** C. Intelligence, https://cambridge-intelligence.com/social-network-analysis/. \ No newline at end of file +- **[8]** C. Intelligence, https://cambridge-intelligence.com/social-network-analysis/. diff --git a/documentations/index.rst b/documentations/index.rst index 220dd78..18539d1 100644 --- a/documentations/index.rst +++ b/documentations/index.rst @@ -12,32 +12,16 @@ Diese sind, um Industriestandards zu entsprechen, auf Englisch gehalten. .. include:: ../CONTRIBUTING.md :parser: myst_parser.sphinx_ - .. toctree:: - :maxdepth: 3 - :caption: Seminararbeiten + :maxdepth: 5 + :caption: Projektdokumentation :numbered: - Ergebnisse/Zwischenbericht_und_Praesentation/PhHo/dev-ops - Ergebnisse/Zwischenbericht_und_Praesentation/TrNo/Ausarbeitung.md - seminararbeiten/Datenspeicherung/00_Datenspeicherung - Ergebnisse/Zwischenbericht_und_Praesentation/TiRo/verflechtungsanalyse.md - - -.. toctree:: - :maxdepth: 3 - :caption: Abschlussberichte - :numbered: - - Ergebnisse/Abschlussbericht_und_Praesentation/TiRo/S2.md - Ergebnisse/Abschlussbericht_und_Praesentation/TiRo/S3.md - Ergebnisse/Abschlussbericht_und_Praesentation/PhHo/05-DEV-OPS - Ergebnisse/Abschlussbericht_und_Praesentation/TrNo/S4-2.md - Ergebnisse/Abschlussbericht_und_Praesentation/TrNo/S4-3-1.md - Ergebnisse/Abschlussbericht_und_Praesentation/PhHo/4-4-2-database-generator - Ergebnisse/Abschlussbericht_und_Praesentation/PhHo/04-data-visualisation-container.md - - Ergebnisse/Abschlussbericht_und_Praesentation/TiRo/S4-5-2.md + Ergebnisse/1_Einleitung.md + Ergebnisse/2_Recherche.md + Ergebnisse/3_Grundlagen.md + Ergebnisse/4_Umsetzung.md + Ergebnisse/Abschlussbericht_und_Praesentation/SeZe/S5-1.md .. toctree:: :glob: diff --git a/documentations/project_management/Arch.-Planning.drawio b/documentations/project_management/Arch.-Planning.drawio index 4c2afbf..354785a 100644 --- a/documentations/project_management/Arch.-Planning.drawio +++ b/documentations/project_management/Arch.-Planning.drawio @@ -1,6 +1,6 @@ - + - + @@ -370,7 +370,7 @@ - + @@ -863,7 +863,7 @@ - + @@ -873,12 +873,12 @@ - - - + + + @@ -897,35 +897,23 @@ - - - - + + + + - - - - + - - - - - - - - - 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Hierbei werden lediglich die engsten Verbindungen visualisiert. Auf dieser Seite ist keine Interaktion mit dem Graphen vorgesehen, da der Benutzer hier lediglich zusätzliche Informationen zum Unternehmen erhalten soll, ohne den gesamten Graphen durchzugehen. Diese Funktion ist für die Homepage vorgesehen. - + ![Abbildung der Companypage](images/Company_page.PNG) ## Bedienung des Netzwerks auf der Person Details Page Die Personen-Detailseite verhält sich in Bezug auf das Netzwerk analog zur Unternehmensseite. Auch hier erhält der Nutzer lediglich zusätzliche Informationen zur Person, kann jedoch nicht weiter mit dem Graphen interagieren. Auf der Personen-Seite werden die Verflechtungen bis zur dritten Ebene abgebildet, und die jeweilige Anzahl der Verbindungen wird oben in den Containern mit den Kennzahlen dargestellt. - + ## Ausgelassene Features Im Kapitel 3.5 wurden neben den hier beschriebenen Funktionen auch weitere Aspekte betrachtet und evaluiert, insbesondere im Hinblick auf die Darstellung und Analyse unternehmerischer Kennzahlen wie EBIT, Umsatz, Gewinn, Aktienkurs usw. . Leider konnten im Zuge der Datenerfassung die hierfür benötigten Daten nicht zuverlässig und in ausreichender Menge generiert werden. Dies führte dazu, dass diese Funktionen aus dem Verflechtungsscope herausgenommen wurden. @@ -61,4 +61,4 @@ Zuletzt ist die Verflechtungsanalyse einer der Bereiche, die abhängig von den E ## Kurzes Resumee der Verflechtungsanalyse/Netzwerkanalyse Alles in allem konnte ein Netzwerk auf Basis der Unternehmensdaten erstellt werden, das Einblicke in die Verzweigungen einiger Unternehmensbeziehungen ermöglicht. Die Analyse wird dabei von klassischen Netzwerkanalyse-Kennzahlen unterstützt. Jedoch muss die Analyse weiterhin durch den Benutzer erfolgen und ist nur in einem eingeschränkteren Maße, als ursprünglich angedacht, durchführbar. Die Analyse hinsichtlich unternehmerischer Kennzahlen musste aufgrund mangelnder Daten aus dem Projekt ausgeschlossen werden. -Die Möglichkeit, ein Netzwerk basierend auf den vorliegenden Unternehmensdaten zu generieren, bietet einen Einblick in die Verästelungen bestimmter Unternehmensbeziehungen. Die Analyse wird durch klassische Netzwerkanalyse-Kennzahlen unterstützt. Dennoch ist die Durchführung der Analyse auf eine begrenztere Ebene beschränkt als ursprünglich geplant. Aufgrund unzureichender Daten im Projekt musste die Analyse bezüglich unternehmerischer Kennzahlen ausgeschlossen werden. \ No newline at end of file +Die Möglichkeit, ein Netzwerk basierend auf den vorliegenden Unternehmensdaten zu generieren, bietet einen Einblick in die Verästelungen bestimmter Unternehmensbeziehungen. Die Analyse wird durch klassische Netzwerkanalyse-Kennzahlen unterstützt. Dennoch ist die Durchführung der Analyse auf eine begrenztere Ebene beschränkt als ursprünglich geplant. Aufgrund unzureichender Daten im Projekt musste die Analyse bezüglich unternehmerischer Kennzahlen ausgeschlossen werden. diff --git a/documentations/Ergebnisse/Abschlussbericht_und_Praesentation/TrNo/S4-2.md b/documentations/Ergebnisse/Abschlussbericht_und_Praesentation/TrNo/S4-2.md index 907b0da..6d9663a 100644 --- a/documentations/Ergebnisse/Abschlussbericht_und_Praesentation/TrNo/S4-2.md +++ b/documentations/Ergebnisse/Abschlussbericht_und_Praesentation/TrNo/S4-2.md @@ -1,4 +1,4 @@ -### 4.2 Lösungsarchitektur +### Lösungsarchitektur (Tristan Nolde) Im Verlauf der Entwicklung stellte sich heraus, dass eine zentrale Stelle zur Datenspeicherung benötigt wird, damit Entwickler Zugang zu den Daten sowohl im Staging-Bereich als auch in der Produktiv-Form haben. Die bereits in der ersten Phase des Projektes angelegte MongoDB im Atlas Hosting (internet-facing) wurde daher durch eine auf der FH-Infrastruktur gehostete Konstruktion ersetzt, die aus einer MongoDB und einer PostgreSQL-Datenbank besteht: Überblick über die Netzwerkarchitektur des FH K8s Clusters diff --git a/documentations/Ergebnisse/Abschlussbericht_und_Praesentation/TrNo/S4-3-1.md b/documentations/Ergebnisse/Abschlussbericht_und_Praesentation/TrNo/S4-3-1.md index f677dff..f501d2e 100644 --- a/documentations/Ergebnisse/Abschlussbericht_und_Praesentation/TrNo/S4-3-1.md +++ b/documentations/Ergebnisse/Abschlussbericht_und_Praesentation/TrNo/S4-3-1.md @@ -1,4 +1,4 @@ -#### Data Extraction +#### Data Extraction (Tristan Nolde) Um die gewünschten Analysen und Visualisierungen zu ermöglichen, wurden verschiedene Implementierungen geschaffen. Diese übernehmen einerseits den Bezug von Rohdaten aus verschiedenen Quellen aber auch die ersten Aufbereitungsschritte und letztlich Persistierung in der Staging DB. diff --git a/documentations/seminararbeiten/Datenspeicherung/00_Datenspeicherung.md b/documentations/Ergebnisse/Zwischenbericht_und_Praesentation/SeZe/00_Datenspeicherung.md similarity index 91% rename from documentations/seminararbeiten/Datenspeicherung/00_Datenspeicherung.md rename to documentations/Ergebnisse/Zwischenbericht_und_Praesentation/SeZe/00_Datenspeicherung.md index 59296e5..43f78f8 100644 --- a/documentations/seminararbeiten/Datenspeicherung/00_Datenspeicherung.md +++ b/documentations/Ergebnisse/Zwischenbericht_und_Praesentation/SeZe/00_Datenspeicherung.md @@ -1,47 +1,7 @@ ![Front](images/Front.PNG) -

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