Home Up PDF Prof. Dr. Ingo Claßen
Zeitreihendatenbanken

Zeitreihen

  • Timestamping: Every data point associated with a specific time
  • Sequential Nature: Data arrives in a continuous, ordered flow
  • High Volumes: Generated at high frequencies, leading to massive datasets
  • Trends, Patterns, and Anomalies: Focus on identifying temporal dynamics

  • Immutability: Typically append-only, rarely updated or deleted

  • Beispiele (link)

  • Charakteristica (link)

  • Datenmodelle (link)

  • Charakteristica (link)

  • Verarbeitung (link)

  • Reguläre/Irreguläre Zeitreihendaten (link)

Beispiele

IOT

  • Predictive Maintenance: Analyzing historical sensor data to predict equipment failures
  • Smart Homes: Monitoring and controlling appliances.27
  • Industrial Automation: Tracking machine performance in real-time
  • Environmental Monitoring: Analyzing air quality, weather patterns, water levels

DevOps and System Monitoring

  • Tracking Infrastructure Metrics: CPU, memory, network performance
  • Monitoring Application Performance: Latency, throughput, error rates
  • Real-Time Alerting for System Anomalies: Notifications based on thresholds or deviations

Financial Markets

  • Tracking Stock Prices and Market Trends: Analyzing economic indicators over time
  • High-Frequency Trading (HFT): Near-instantaneous response times
  • Algorithmic Trading: Automated strategies based on real-time and historical data
  • Risk Management: Monitoring financial metrics over time
  • Market Analysis: Identifying patterns and trends for investment decisions

Other Use Cases

  • Healthcare: Continuous patient vitals monitoring
  • Energy Sector: Utility usage management, grid optimization
  • Environmental Monitoring: Tracking climate change, weather patterns
  • Product Analytics: Tracking user interactions with applications
  • Website Traffic Analysis: Understanding user journeys
  • Logistics and Asset Tracking: Real-time monitoring of shipments
  • Anomaly Detection: Identifying unusual patterns across domains

Kardinalität

Zeitbasierte Analyse

Konzepte Zeitreihendatenbanksysteme

  • Time as Primary Index: Architecturally designed with timestamp as core organizing principle.Faster writes and efficient time-based queries
  • Optimized for Sequential Data: Engineered for continuous, append-only data streams
  • Built-in Data Retention Policies: Automated expiration or downsampling of older data
  • Time-Aware Data Structures: Time partitioning (chunking) for efficient querying. Specialized compression algorithms

  • Specialized Query Languages: Optimized for time-based analysis (bucketing, moving averages)

  • Columnar Storage: Stores data by columns, improving analytical query performance and- compression
  • Time-Based Partitioning: Divides data into time-based segments (chunks) for efficient- management and querying
  • Specialized Indexing: Optimized for time ranges, enabling fast retrieval within temporal- boundaries
  • Data Compression: Tailored algorithms to exploit temporal coherence and value patterns.2
  • In-Memory Processing: For recent and frequently accessed data, enabling low-latency operations

Zeitreihendatenbanksysteme

InfluxDB

Prometheus

Kdb

Graphite

TimescaleDB

QuestDB

Apache Druid

GridDB

TDengine