Data Tactics provides systems engineering and integration services focused on big data architecture and unified data spaces. They have experience architecting solutions managing petabytes of data across multiple security enclaves and domains. Their unified data space approach aims to break down data silos by incorporating rich semantic context and metadata across structured, unstructured, and multi-modal data sources to enable integration, enrichment, and exploration of data.

1. Data Tactics
Unified DataSpace
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2. Cloud
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3. Systems Engineering & Integration
SYSTEMS ENGINEERING
• Data Ingestion Frameworks (structured, unstructured, semi-structured)
• Semantic DataSpace Enrichment SYSTEM INTEGRATION
• Cloud Management Systems (CMS) • Ingestion
• Cloudbase/Accumulo – Generalized Ingest /
– Pig (Big Data) Plug-in NiagraFiles
• Dissemination and Reporting Tools • Geospatial Capabilities
• Data Mining, Exploitation, and Correlation Tools • Biometric Capabilities
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4. Cloud Experience
17 Enclaves at SECRET//NOFORN 4 Enclaves for NATO ISAF
• 3 in Tyson’s • 2 in Afghanistan
• 1 at GISA, Ft. Bragg • 1 at GISA, Fort Bragg
• 2 in Hawaii • 1 in Germany
• 2 in Germany US BICES Cloud in Germany
• 7 at Aberdeen Over a dozen at UNCLASS//FOUO
• 2 in Afghanistan • Supporting real-world missions on
6 Enclaves at TS//SCI contract
• AF TENCAP • At various levels of complexity
• NRL
• DARPA
• INSCOM
• DCGS-A
• DHS OI&A
Cloud Domains is where we live
Data, is the Hard Problem
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5. Data – The Hard Part
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6. BigData Architecture
Data Tactics has delivered solutions that manage PETABYTES of data
and provide mission relevant analytics, metrics and user interfaces
• DESIGN, DEVELOPMENT AND INTEGRATION OF REFERENCE ARCHITECTURES
– Ghost Machine
– Stratus
• SECURE DATABASE ARCHITECTURES
– Secure Entity Database (SED)
– Defense Cross-Domain Analytic Capability (DCAC)
• DATA MIGRATION, EXTRACTION, TRANSFORM AND PARSING
• FEDERATED DATA MANAGEMENT
– Federated Search, Multi-Source / Multi-Vendor Integration
– Storage Cluster Management
• DATA MINING AND FORENSIC ANALYSIS
• SPATIAL, MULTI-DOMAIN, AND CLOUD DATA SERVICES
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7. Unified DataSpace
The Wild
• Data sources
with rich data & Segment 3 - Model Description
semantic context
locked in domain Data
Rich semantic
silos Models
context
• Data tightly
coupled to
data-models
• Data-models
Segment 2 - Data Description
tightly coupled to Structured
Integration
Enrichment
storage models Data Exploitation
Exploration
Silos isolated by Across all sources
• Implementation Segment 1 - Artifact Description
technology
• Storage structure Unstructured Rich data
• Data Data context
representation
• Data modality
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8. Unified DataSpace
High-Level Conceptual Model of the DataSpace
and Ingest/Extraction Flows
Segment 3 - Model Semantics
.
2 .
2
CONCEPT CONCEPT_ASSOCIATION PREDICATE PREDICATE_ASSOCIATION
.
.
.
Uses Uses
Segment 1 - Artifact Semantics Segment 2 - Data Semantics Semantics
SOURCE . +
2 . 2 . Metadata
ARTIFACT ARTIFACT_ASSOCIATION TERM . STATEMENT
. .
. .
Data
Uses +
Metadata Metadata
Segment 0 - Artifacts
Ingest Extraction
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9. Unified DataSpace
•Segment 0 is an artifact store (i.e., binary
representation of artifacts). High-Level Conceptual Model of the DataSpace
and Ingest/Extraction Flows
Segment 3 - Model Semantics
•Segment 1 represents artifact semantics
.
2 .
2
CONCEPT CONCEPT_ASSOCIATION PREDICATE PREDICATE_ASSOCIATION
.
.
.
and includes artifact metadata and Uses Uses
associations between the artifacts. Indexing Segment 1 - Artifact Semantics Segment 2 - Data Semantics Semantics
of Segment 1 supports search on text
SOURCE . +
2 . 2 . Metadata
ARTIFACT ARTIFACT_ASSOCIATION TERM . STATEMENT
. .
.
content, geospatial, and artifact meta data.
.
Data
Uses +
Metadata Metadata
Segment 0 - Artifacts
•Segment 2 represents data and semantics
Ingest Extraction
of structured data elements extracted from
artifacts. Indexing of Segment 2 supports
search on properties of entities (e.g., Person,
Location) based on their properties and
relationships.
•Segment 3 represents data-models
extracted from artifacts and models used for
aligning, disambiguating, and enriching the
elements of Segments 1 and 2.
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10. Data Description Framework
• DDF – looks at data in the following ways
– Mention: A chunk of data, either physically located within a tangible artifact,
or contained within an analyst’s mind
• “Washington” at offset x in file Y
– Sign: A representation of all disambiguated mentions that are identical except
for their indexicality
• E.g., “Washington”
– Concept: An abstract idea, defined explicitly or implicitly by a source data-
model
• E.g., City, Person, Name, Address, Photo
– Predicate: An abstract idea used to express a relationship between “things”
• E.g., isCity, isPerson, hasName, hasAddress, hasPhoto
– Term: A disambiguated sign abstracted from the source artifact or asserting
analyst
• E.g., Washington Person; Washington Location
– Statement: Encodes a binary relationship between a subject (term) and an
object mediated by a predicate
• E.g.,[Washington, Person] hasPhoto [GeorgeWashingtonImage.jpg]
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11. Unified DataSpace
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12. Data Model Example
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13. DataSpace Workbench
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14. Elastic Data Ingest
Java Messaging Service
Artifact Processor Persistence Index Manager Error Manager
Queue Manager Queue Queue Queue
Queue Artifact Persistence Index Error
Loader Processor Manager Manager Manager
UDS Components
Lucene
File
System
Custom Components
Hadoop DFS
Artifact Processor Persistence
Modules Manager Modules
BigTable
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