Search and Discovery¶

OpenML Crawler provides powerful search and discovery capabilities to help users find, explore, and understand available data sources. The search system includes metadata indexing, semantic search, data cataloging, and intelligent discovery features.

Data Catalog¶

Catalog Architecture¶

The data catalog serves as a centralized repository of metadata about all available data sources:

from openmlcrawler.search import DataCatalog

catalog = DataCatalog()

# Initialize catalog
catalog.initialize(
    storage_backend="postgresql",  # or "elasticsearch", "mongodb"
    metadata_schema="extended",
    indexing_enabled=True
)

# Register data sources
catalog.register_sources([
    {
        "name": "weather_api",
        "type": "api",
        "description": "Global weather data from multiple providers",
        "tags": ["weather", "meteorology", "climate"],
        "schema": weather_schema,
        "update_frequency": "hourly",
        "quality_score": 0.95
    },
    {
        "name": "financial_data",
        "type": "database",
        "description": "Stock market and financial indicators",
        "tags": ["finance", "stocks", "economics"],
        "schema": finance_schema,
        "update_frequency": "daily",
        "quality_score": 0.98
    }
])

Metadata Management¶

Comprehensive metadata collection and management:

from openmlcrawler.search import MetadataManager

manager = MetadataManager()

# Extract metadata from data sources
metadata = manager.extract_metadata(
    data_source=source_config,
    extraction_rules={
        "basic": ["name", "description", "type", "size"],
        "technical": ["schema", "format", "encoding", "compression"],
        "quality": ["completeness", "accuracy", "freshness"],
        "usage": ["popularity", "access_patterns", "dependencies"]
    }
)

# Enrich metadata with additional information
enriched_metadata = manager.enrich_metadata(
    metadata=metadata,
    enrichment_sources=[
        "data_lineage",
        "business_glossary",
        "data_stewardship",
        "usage_analytics"
    ]
)

# Update metadata in catalog
manager.update_catalog_metadata(
    source_id="weather_api",
    metadata=enriched_metadata,
    version_control=True
)

Search Engine¶

Full-Text Search¶

Advanced full-text search capabilities:

from openmlcrawler.search import SearchEngine

engine = SearchEngine()

# Basic text search
results = engine.search(
    query="climate change temperature",
    search_type="full_text",
    filters={
        "data_type": "time_series",
        "date_range": ("2020-01-01", "2023-12-31"),
        "quality_score": {"min": 0.8}
    },
    limit=50
)

# Advanced search with operators
advanced_results = engine.advanced_search(
    query="""
    (temperature OR weather) AND
    (climate OR meteorological) AND
    location:(United States OR Canada)
    """,
    search_fields=["description", "tags", "schema_fields"],
    fuzzy_matching=True,
    phrase_boost=2.0
)

Semantic Search¶

AI-powered semantic search for natural language queries:

from openmlcrawler.search import SemanticSearch

semantic_engine = SemanticSearch()

# Semantic search with natural language
semantic_results = semantic_engine.semantic_search(
    query="I need data about how weather affects crop yields in farming",
    embedding_model="sentence-transformers",
    similarity_threshold=0.7,
    rerank_results=True
)

# Multi-modal search (text + metadata)
multimodal_results = semantic_engine.multimodal_search(
    text_query="economic indicators",
    metadata_filters={
        "update_frequency": "daily",
        "geographic_coverage": "global"
    },
    cross_references=True
)

# Query expansion
expanded_query = semantic_engine.expand_query(
    original_query="stock prices",
    expansion_type="synonym",  # or "related_terms", "domain_specific"
    max_expansions=5
)

Faceted Search¶

Search with multiple facets and filters:

from openmlcrawler.search import FacetedSearch

faceted_engine = FacetedSearch()

# Configure facets
facets = faceted_engine.configure_facets([
    {
        "name": "data_type",
        "type": "categorical",
        "values": ["tabular", "time_series", "text", "image", "geospatial"]
    },
    {
        "name": "domain",
        "type": "categorical",
        "values": ["finance", "healthcare", "weather", "social", "government"]
    },
    {
        "name": "quality_score",
        "type": "range",
        "min": 0.0,
        "max": 1.0,
        "step": 0.1
    },
    {
        "name": "last_updated",
        "type": "date_range",
        "format": "YYYY-MM-DD"
    }
])

# Faceted search
faceted_results = faceted_engine.faceted_search(
    query="economic data",
    active_facets={
        "domain": ["finance", "economics"],
        "quality_score": {"min": 0.8},
        "last_updated": {"from": "2023-01-01"}
    },
    facet_counts=True
)

Data Discovery¶

Intelligent Recommendations¶

AI-powered data discovery and recommendations:

from openmlcrawler.search import DataDiscovery

discovery = DataDiscovery()

# Discover related datasets
related_datasets = discovery.discover_related(
    source_dataset="customer_transactions",
    relationship_type="similar_schema",  # or "complementary", "derived_from"
    max_results=10
)

# Recommend datasets for analysis
recommendations = discovery.recommend_datasets(
    user_profile={
        "interests": ["finance", "economics"],
        "previous_usage": ["stock_data", "economic_indicators"],
        "analysis_goals": ["trend_analysis", "forecasting"]
    },
    context="market_research",
    diversity_factor=0.3
)

# Discover data patterns
patterns = discovery.discover_patterns(
    data_sample=input_data,
    pattern_types=["temporal", "spatial", "correlation"],
    min_confidence=0.7
)

Automated Tagging¶

Automatic tagging and categorization of data:

from openmlcrawler.search import AutoTagger

tagger = AutoTagger()

# Automatic tagging
tags = tagger.auto_tag(
    data=input_data,
    tagging_methods=[
        "content_analysis",
        "metadata_inference",
        "usage_patterns",
        "domain_expertise"
    ],
    confidence_threshold=0.6
)

# Tag validation and refinement
validated_tags = tagger.validate_tags(
    tags=tags,
    validation_sources=[
        "expert_review",
        "user_feedback",
        "cross_reference"
    ]
)

# Hierarchical tagging
hierarchical_tags = tagger.create_hierarchy(
    tags=tags,
    hierarchy_rules={
        "finance": ["stocks", "bonds", "derivatives"],
        "healthcare": ["clinical", "administrative", "research"]
    }
)

Data Lineage Tracking¶

Track data lineage and dependencies:

from openmlcrawler.search import LineageTracker

tracker = LineageTracker()

# Track data lineage
lineage = tracker.track_lineage(
    data_source="processed_sales_data",
    include_upstream=True,
    include_downstream=True,
    max_depth=5
)

# Visualize lineage
visualization = tracker.visualize_lineage(
    lineage=lineage,
    format="graphviz",
    include_metadata=True
)

# Impact analysis
impact = tracker.analyze_impact(
    source_change="customer_table_schema_update",
    affected_downstream=lineage["downstream"],
    impact_types=["breaking", "performance", "data_quality"]
)

Search Analytics¶

Usage Analytics¶

Analyze search patterns and user behavior:

from openmlcrawler.search import SearchAnalytics

analytics = SearchAnalytics()

# Analyze search patterns
patterns = analytics.analyze_search_patterns(
    search_logs=search_logs,
    time_range=("2023-01-01", "2023-12-31"),
    analysis_types=[
        "popular_queries",
        "failed_searches",
        "query_trends",
        "user_segments"
    ]
)

# User behavior analysis
behavior = analytics.analyze_user_behavior(
    user_logs=user_logs,
    behavior_metrics=[
        "search_frequency",
        "query_complexity",
        "result_interaction",
        "discovery_patterns"
    ]
)

# Generate insights
insights = analytics.generate_insights(
    patterns=patterns,
    behavior=behavior,
    insight_types=[
        "search_optimization",
        "content_gaps",
        "user_personas"
    ]
)

Performance Monitoring¶

Monitor search system performance:

from openmlcrawler.search import SearchMonitor

monitor = SearchMonitor()

# Monitor search performance
performance = monitor.monitor_performance(
    metrics=[
        "query_latency",
        "index_size",
        "cache_hit_rate",
        "error_rate",
        "throughput"
    ],
    time_window="1_hour"
)

# Performance optimization
optimization = monitor.optimize_performance(
    performance_metrics=performance,
    optimization_strategies=[
        "index_optimization",
        "cache_tuning",
        "query_rewriting",
        "infrastructure_scaling"
    ]
)

# Generate performance reports
report = monitor.generate_performance_report(
    performance=performance,
    optimization=optimization,
    time_period="weekly"
)

Advanced Features¶

Federated Search¶

Search across multiple data catalogs:

from openmlcrawler.search import FederatedSearch

federated = FederatedSearch()

# Configure federated search
federated.configure_federation([
    {
        "name": "internal_catalog",
        "endpoint": "http://internal-catalog:8080",
        "authentication": "oauth2"
    },
    {
        "name": "partner_catalog",
        "endpoint": "https://partner-catalog.com/api",
        "authentication": "api_key"
    },
    {
        "name": "public_catalog",
        "endpoint": "https://data.gov/catalog",
        "authentication": None
    }
])

# Federated search
federated_results = federated.search_federated(
    query="environmental data",
    catalogs=["internal_catalog", "partner_catalog"],
    result_aggregation="merge",  # or "rank", "cluster"
    max_results_per_catalog=20
)

Real-time Search¶

Real-time indexing and search capabilities:

from openmlcrawler.search import RealTimeSearch

realtime = RealTimeSearch()

# Real-time indexing
indexer = realtime.create_realtime_index(
    index_name="live_data",
    data_sources=["streaming_api", "database_changes"],
    indexing_strategy="incremental",
    refresh_interval="30_seconds"
)

# Real-time search
live_results = realtime.search_realtime(
    query="recent transactions",
    time_window="5_minutes",
    freshness_requirement="near_real_time"
)

# Streaming search results
result_stream = realtime.stream_search_results(
    query="breaking news",
    update_interval="10_seconds",
    result_format="json_stream"
)

Machine Learning Integration¶

ML-powered search features:

from openmlcrawler.search import MLSearch

ml_search = MLSearch()

# Query understanding
query_intent = ml_search.understand_query(
    query="show me sales data for last quarter",
    intent_classification=True,
    entity_extraction=True
)

# Result ranking
ranked_results = ml_search.rank_results(
    query=query,
    results=raw_results,
    ranking_model="bert_reranker",
    features=["relevance", "freshness", "popularity", "authority"]
)

# Query expansion with ML
expanded_results = ml_search.expand_with_ml(
    query="machine learning datasets",
    expansion_model="word2vec",
    context_sources=["user_history", "domain_knowledge"],
    diversity_weight=0.3
)

Configuration¶

Search Configuration¶

Configure search system settings:

search:
  engine:
    type: "elasticsearch"  # or "solr", "opensearch"
    host: "localhost"
    port: 9200
    index_prefix: "openml_"

  indexing:
    batch_size: 1000
    refresh_interval: "30s"
    replica_count: 1
    shard_count: 3

  search:
    default_operator: "AND"
    fuzzy_matching: true
    phrase_slop: 2
    max_expansions: 50

  semantic:
    model: "sentence-transformers/all-MiniLM-L6-v2"
    similarity_threshold: 0.7
    cache_embeddings: true

  discovery:
    recommendation_engine: "collaborative_filtering"
    pattern_detection: true
    auto_tagging: true

Catalog Configuration¶

Configure data catalog settings:

catalog:
  storage:
    backend: "postgresql"
    connection_string: "postgresql://user:pass@localhost/openml"
    schema: "catalog"

  metadata:
    schema_version: "2.0"
    auto_extraction: true
    enrichment_enabled: true
    version_control: true

  indexing:
    full_text_index: true
    semantic_index: true
    facet_index: true
    realtime_index: false

  security:
    access_control: "rbac"
    audit_logging: true
    encryption: true

Best Practices¶

Search Optimization¶

Query Analysis: Understand user intent and context
Index Optimization: Maintain efficient search indexes
Result Ranking: Use relevance algorithms for better results
Caching Strategy: Implement intelligent caching for performance
Query Expansion: Use synonyms and related terms
Personalization: Customize results based on user preferences

Catalog Management¶

Metadata Quality: Ensure comprehensive and accurate metadata
Consistent Tagging: Use standardized tags and categories
Regular Updates: Keep catalog information current
Access Control: Implement appropriate security controls
Usage Tracking: Monitor and analyze catalog usage patterns
Data Governance: Establish data stewardship processes

Discovery Enhancement¶

User Profiling: Build user profiles for better recommendations
Context Awareness: Consider user context and session information
Collaborative Filtering: Use user behavior for recommendations
Content Analysis: Analyze data content for better categorization
Feedback Loop: Incorporate user feedback for improvement
Trend Analysis: Identify emerging data needs and trends

Performance Tuning¶

Index Management: Optimize index size and structure
Query Optimization: Use efficient query patterns
Caching Layers: Implement multiple caching levels
Load Balancing: Distribute search load across multiple nodes
Monitoring: Track performance metrics and bottlenecks
Scaling: Plan for horizontal and vertical scaling

Troubleshooting¶

Common Search Issues¶

Low Result Quality¶

Issue: Search returns irrelevant results
Solution: Improve query understanding, update relevance algorithms, add better metadata

Slow Search Performance¶

Issue: Search queries are slow
Solution: Optimize indexes, implement caching, tune search parameters

Index Synchronization¶

Issue: Search index out of sync with data
Solution: Implement real-time indexing, regular index refreshes, monitor sync status

Common Discovery Issues¶

Poor Recommendations¶

Issue: Data recommendations are not relevant
Solution: Improve user profiling, update recommendation algorithms, gather more usage data

Missing Metadata¶

Issue: Incomplete data catalog information
Solution: Enhance metadata extraction, implement data profiling, add manual curation

Access Control Problems¶

Issue: Users can't access expected data
Solution: Review access control policies, update user permissions, audit access logs