features_temporal_queries

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category: "📊 Data Features" version: "v1.3.0" status: "✅" date: "22.12.2025"

🝒 Temporal Time-Range Queries

Point-in-Time Queries und Temporal Joins für Zeitbereichs-Abfragen.

📋 Inhaltsverzeichnis

• 📋 Übersicht
• ✨ Features
• 🚀 Schnellstart
• 📖 Detaillierte Dokumentation
• 💡 Best Practices
• 🔧 Troubleshooting
• 📚 Siehe auch
• 📝 Changelog

📋 Übersicht

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Status: ✅ Implemented & Tested (8/8 tests passing)
Feature: Extended temporal graph queries with time-window filtering
Date: 2025-01-15

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Overview

This feature extends Themis's temporal graph capabilities from single-timestamp queries to time-range queries. You can now find all edges that overlap with or are fully contained within a specified time window.

Use Cases

• Audit Queries: "Show all relationships valid during Q4 2024"
• Compliance: "Find edges fully contained within investigation period"
• Historical Analysis: "What connections existed between 2020-2022?"
• Temporal Analytics: "Relationships overlapping with event timeframe"

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API Reference

TimeRangeFilter Structure

struct TimeRangeFilter {
    int64_t start_ms;  // Range start (milliseconds since epoch)
    int64_t end_ms;    // Range end (milliseconds since epoch)
    
    // Factory methods
    static TimeRangeFilter between(int64_t start, int64_t end);
    static TimeRangeFilter since(int64_t start);
    static TimeRangeFilter until(int64_t end);
    static TimeRangeFilter all();
    
    // Filtering methods
    bool hasOverlap(std::optional<int64_t> edge_valid_from, 
                    std::optional<int64_t> edge_valid_to) const;
    bool fullyContains(std::optional<int64_t> edge_valid_from,
                       std::optional<int64_t> edge_valid_to) const;
};

EdgeInfo Structure

struct EdgeInfo {
    std::string edgeId;                     // Edge identifier
    std::string fromPk;                     // Source node primary key
    std::string toPk;                       // Target node primary key
    std::optional<int64_t> valid_from;      // Edge valid from (ms)
    std::optional<int64_t> valid_to;        // Edge valid to (ms)
};

Query Methods

Global Time-Range Query

std::pair<Status, std::vector<EdgeInfo>> 
getEdgesInTimeRange(int64_t range_start_ms, 
                    int64_t range_end_ms,
                    bool require_full_containment = false) const;

Parameters:

• range_start_ms: Query time window start (milliseconds since epoch)
• range_end_ms: Query time window end (milliseconds since epoch)
• require_full_containment:
  • false (default): Returns edges with any overlap with query window
  • true: Returns edges fully contained within query window

Returns:

• Status: Operation success/failure
• vector<EdgeInfo>: All matching edges with temporal metadata

Time Complexity: O(E) where E = total edges in database

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Node-Specific Time-Range Query

std::pair<Status, std::vector<EdgeInfo>>
getOutEdgesInTimeRange(std::string_view fromPk,
                       int64_t range_start_ms,
                       int64_t range_end_ms, 
                       bool require_full_containment = false) const;

Parameters:

• fromPk: Source node primary key
• range_start_ms: Query time window start (milliseconds since epoch)
• range_end_ms: Query time window end (milliseconds since epoch)
• require_full_containment: Same as global query

Returns:

• Status: Operation success/failure
• vector<EdgeInfo>: All matching outgoing edges from fromPk

Time Complexity: O(d) where d = out-degree of node

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Usage Examples

Example 1: Overlap Query (Default)

Find all edges with any overlap with time window [1000, 2000]:

GraphIndexManager graph(db);

// Add edges with different temporal periods
BaseEntity e1("edge1");
e1.setField("_from", "A");
e1.setField("_to", "B");
e1.setField("valid_from", 500);   // Partially overlaps
e1.setField("valid_to", 1500);
graph.addEdge(e1);

BaseEntity e2("edge2");
e2.setField("_from", "A");
e2.setField("_to", "C");
e2.setField("valid_from", 1200);  // Fully inside
e2.setField("valid_to", 1800);
graph.addEdge(e2);

BaseEntity e3("edge3");
e3.setField("_from", "B");
e3.setField("_to", "C");
e3.setField("valid_from", 2500);  // No overlap
e3.setField("valid_to", 3000);
graph.addEdge(e3);

// Query: Find edges overlapping [1000, 2000]
auto [status, edges] = graph.getEdgesInTimeRange(1000, 2000);

// Result: edges = [edge1, edge2]
// edge1: overlaps (500-1500 overlaps with 1000-2000)
// edge2: fully inside (1200-1800 inside 1000-2000)
// edge3: no overlap (2500-3000 is after 2000)

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Example 2: Full Containment Query

Find edges fully contained within time window [1000, 3000]:

// Same edges as Example 1

// Query: Find edges FULLY INSIDE [1000, 3000]
auto [status, edges] = graph.getEdgesInTimeRange(1000, 3000, true);

// Result: edges = [edge2, edge3]
// edge1: NOT included (500-1500 starts before 1000)
// edge2: included (1200-1800 fully inside 1000-3000)
// edge3: included (2500-3000 fully inside 1000-3000)

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Example 3: Node-Specific Time-Range Query

Find outgoing edges from specific node in time window:

// Add edges from node "user1"
BaseEntity e1("follow1");
e1.setField("_from", "user1");
e1.setField("_to", "user2");
e1.setField("valid_from", 1000000);
e1.setField("valid_to", 2000000);
graph.addEdge(e1);

BaseEntity e2("follow2");
e2.setField("_from", "user1");
e2.setField("_to", "user3");
e2.setField("valid_from", 1500000);
e2.setField("valid_to", 2500000);
graph.addEdge(e2);

BaseEntity e3("follow3");
e3.setField("_from", "user2");  // Different source!
e3.setField("_to", "user3");
e3.setField("valid_from", 1200000);
e3.setField("valid_to", 1800000);
graph.addEdge(e3);

// Query: Find user1's outgoing edges in [1100000, 1900000]
auto [status, edges] = graph.getOutEdgesInTimeRange("user1", 1100000, 1900000);

// Result: edges = [follow1, follow2]
// follow1: from user1, overlaps query window
// follow2: from user1, overlaps query window
// follow3: NOT included (from user2, not user1)

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Example 4: Unbounded Edges (Always Valid)

Edges without valid_from/valid_to match all time queries:

BaseEntity unbounded("always_active");
unbounded.setField("_from", "A");
unbounded.setField("_to", "B");
// NO valid_from/valid_to fields = unbounded temporal range
graph.addEdge(unbounded);

BaseEntity bounded("temporary");
bounded.setField("_from", "A");
bounded.setField("_to", "C");
bounded.setField("valid_from", 1000);
bounded.setField("valid_to", 2000);
graph.addEdge(bounded);

// Query: Find edges in [500, 1500]
auto [status, edges] = graph.getEdgesInTimeRange(500, 1500);

// Result: edges = [always_active, temporary]
// always_active: unbounded edges always included
// temporary: 1000-2000 overlaps 500-1500

Aggregate numeric edge property across a time window

You can now aggregate a numeric property (SUM, AVG, MIN, MAX, COUNT) across all edges that match a time window. The aggregation supports an optional edge type filter (_type).

std::pair<Status, TemporalAggregationResult>
aggregateEdgePropertyInTimeRange(
  std::string_view property,                // field name on edge entity, e.g. "cost" or "_weight"
  Aggregation agg,                          // COUNT, SUM, AVG, MIN, MAX
  int64_t range_start_ms,
  int64_t range_end_ms,
  bool require_full_containment = false,
  std::optional<std::string_view> edge_type = std::nullopt
) const;

Behavior notes:

• COUNT returns the number of matching edges (regardless of whether the property exists).
• SUM/AVG/MIN/MAX consider only edges that have a numeric value for the requested property.
• edge_type (optional) allows server-side filtering by _type.

Example:

// Sum cost of edges of type "A" overlapping [1000,2000]
auto [st, res] = graph.aggregateEdgePropertyInTimeRange("cost", GraphIndexManager::Aggregation::SUM, 1000, 2000, false, std::string_view("A"));
if (st.ok) {
  std::cout << "count=" << res.count << " sum=" << res.value << "\n";
}

Implementation detail: edge entities are stored under keys like edge:<edgeId>. To be robust across historical formats, the implementation also attempts edge:<graphId>:<edgeId> when reading entities and falls back to edge:<edgeId> if necessary. Helper functions getEdgeType_() and getEdgeWeight_() were hardened to try both formats.

Filtering Semantics

Overlap vs. Full Containment

Overlap (require_full_containment = false):

• Default behavior
• Returns edges with any temporal overlap with query window
• Includes partially overlapping edges
• Formula: edge_start <= query_end AND edge_end >= query_start

Full Containment (require_full_containment = true):

• Strict containment
• Returns edges fully inside query window
• Excludes partially overlapping edges
• Formula: edge_start >= query_start AND edge_end <= query_end

TimeRangeFilter Behavior

Edge Period	Query Window	hasOverlap()	fullyContains()
[500, 1500]	[1000, 2000]	✅ true	❌ false
[1200, 1800]	[1000, 2000]	✅ true	✅ true
[2500, 3000]	[1000, 2000]	❌ false	❌ false
[null, null]	[1000, 2000]	✅ true	✅ true
[500, null]	[1000, 2000]	✅ true	❌ false
[null, 3000]	[1000, 2000]	✅ true	❌ false

Unbounded Edges:

• Edges without valid_from/valid_to are treated as unbounded (always valid)
• hasOverlap() always returns true for unbounded edges
• fullyContains() always returns true for unbounded edges

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Performance Characteristics

Global Query: getEdgesInTimeRange()

• Time Complexity: O(E) where E = total edges in database
• Space Complexity: O(R) where R = number of matching edges
• Database Scans: Full scan of graph:out:* prefix
• Entity Loads: One db.get("edge:*") per edge

Optimization Opportunities:

• Add temporal index for bounded time ranges
• Sorted temporal B-tree for range scans
• Materialized views for common time windows

Node-Specific Query: getOutEdgesInTimeRange()

• Time Complexity: O(d) where d = out-degree of source node
• Space Complexity: O(R) where R = number of matching edges
• Database Scans: Prefix scan of graph:out:<fromPk>:*
• Entity Loads: One db.get("edge:*") per outgoing edge

Much Faster Than Global Query:

• Only scans edges from specific node
• Leverages existing graph:out: adjacency index
• Suitable for high-frequency queries on specific nodes

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Implementation Details

Key Schema

# Edge entity storage
edge:<edge_id> -> BaseEntity(id, _from, _to, valid_from, valid_to, ...)

# Graph adjacency indices (temporal data stored in entity, not index)
graph:out:<from_pk>:<edge_id> -> <to_pk>
graph:in:<to_pk>:<edge_id> -> <from_pk>

Design Choice:

• Temporal fields (valid_from, valid_to) stored in edge entity, not in index keys
• Requires entity load to check temporal bounds
• Simplifies index structure (no temporal key encoding)
• Trade-off: Extra db.get() per edge vs. complex temporal index

Algorithm (getEdgesInTimeRange)

1. Create TimeRangeFilter from query parameters
2. Scan all edges with prefix "graph:out:"
3. For each edge key "graph:out:<from>:<edgeId>":
   a. Parse edgeId from key
   b. Load edge entity from "edge:<edgeId>"
   c. Extract valid_from, valid_to fields
   d. Check temporal match (overlap or containment)
   e. If match, add EdgeInfo to results
4. Return filtered results

Algorithm (getOutEdgesInTimeRange)

1. Create TimeRangeFilter from query parameters
2. Scan edges with prefix "graph:out:<fromPk>:"
3. For each edge (same as global query):
   a-e. (identical to global query)
4. Return filtered results

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Testing

Test Coverage (8/8 Passing)

1. TimeRangeFilter_Overlap - Filter logic: overlap detection
2. TimeRangeFilter_FullContainment - Filter logic: containment check
3. GetEdgesInTimeRange_Overlap - Global query: overlap mode
4. GetEdgesInTimeRange_FullContainment - Global query: containment mode
5. GetOutEdgesInTimeRange - Node-specific query: basic functionality
6. GetOutEdgesInTimeRange_NoMatch - Node-specific query: no results
7. UnboundedEdges_AlwaysIncluded - Unbounded edges match all queries
8. EdgeInfo_ContainsTemporalData - Result structure validation

Test File

# Run all time-range tests
./themis_tests --gtest_filter="TimeRangeQueryTest.*"

# Expected output:
# [  PASSED  ] 8 tests.

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Integration with Existing Features

Works With Recursive Path Queries

// Step 1: Find temporal path
RecursivePathQuery rpq;
rpq.start_node = "user1";
rpq.end_node = "user5";
rpq.max_depth = 3;
rpq.valid_from = 1500000;  // Single timestamp
rpq.valid_to = 1500000;
auto [status, path] = queryEngine.executeRecursivePathQuery(rpq);

// Step 2: Verify all edges in path valid during time window
auto [st, edges] = graph.getEdgesInTimeRange(1400000, 1600000);
for (const auto& edgeInfo : edges) {
    // Check if edge in path is valid throughout window
}

Temporal Graph Capabilities

Feature	Single Timestamp	Time Range	Status
BFS/Dijkstra at time T	✅ bfsAtTime()	❌	Implemented
Shortest path at time T	✅ dijkstraAtTime()	❌	Implemented
Find edges in window	❌	✅ getEdgesInTimeRange()	Implemented ✨
Find node edges in window	❌	✅ getOutEdgesInTimeRange()	Implemented ✨
Temporal aggregation	❌	✅ getTemporalStats()	Implemented ✨

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Future Enhancements

1. Temporal Index

Problem: O(E) scan for global queries
Solution: B-tree index on (valid_from, valid_to) pairs

// Hypothetical API
auto edges = graph.getEdgesInTimeRange_Indexed(1000, 2000);
// Time complexity: O(log E + R) vs. current O(E)

2. Time-Window Path Queries

Problem: Current path queries use single timestamp
Solution: Extend RecursivePathQuery with time windows

RecursivePathQuery rpq;
rpq.window_start = 1000000;
rpq.window_end = 2000000;
// Find paths where ALL edges valid during [1000000, 2000000]

3. Temporal Aggregations

✅ Implemented! Temporal statistics now available:

auto [status, stats] = graph.getTemporalStats(1000, 2000);
// Returns: TemporalStats{
//   edge_count, fully_contained_count, bounded_edge_count,
//   avg_duration_ms, total_duration_ms, min/max_duration_ms,
//   earliest_start, latest_end
// }
std::cout << stats.toString();

Features:

• Count edges with overlap or full containment
• Duration statistics (AVG, SUM, MIN, MAX) for bounded edges
• Temporal range coverage (earliest start, latest end)
• 6/6 tests passing ✅

4. Streaming Time-Range Queries

Problem: Large result sets exhaust memory
Solution: Iterator-based API

auto iter = graph.streamEdgesInTimeRange(1000, 2000);
while (iter.hasNext()) {
    EdgeInfo edge = iter.next();
    // Process one edge at a time
}

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Known Limitations

1. No Temporal Index: Global queries scan all edges (O(E))
2. Entity Load Overhead: One db.get() per edge (network/disk I/O)
3. No Streaming API: Large result sets loaded into memory
4. No Temporal Joins: Cannot join time-range results with other queries
5. No Unbounded Query Optimization: Unbounded edges checked even when range is bounded

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Changelog

• 2025-01-15: Initial implementation

  • Added TimeRangeFilter structure to temporal_graph.h
  • Added EdgeInfo structure to graph_index.h
  • Implemented getEdgesInTimeRange() in graph_index.cpp
  • Implemented getOutEdgesInTimeRange() in graph_index.cpp
  • Created 8 comprehensive tests (all passing)
  • Documentation created

• 2025-11-11: Temporal aggregation property support

  • Added aggregateEdgePropertyInTimeRange() supporting COUNT, SUM, AVG, MIN, MAX over a query time window
  • Added unit tests test_temporal_aggregation_property.cpp (4 tests) and updated CMake
  • Hardened entity read paths: getEdgeType_() and getEdgeWeight_() try both edge:<graphId>:<edgeId> and edge:<edgeId> formats to be backward-compatible
  • All new tests passing

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See Also

• Recursive Path Queries - Multi-hop temporal reasoning
• Temporal Graph Design - Overall temporal architecture
• Graph Index - Adjacency index design
• MVCC Design - Transaction temporal semantics