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Berkeley DB (BDB) is an embedded database software library for key/value data, historically significant in open-source software. Berkeley DB is written in C with API bindings for many other programming languages. BDB stores arbitrary key/data pairs as byte arrays and supports multiple data items for a single key. Berkeley DB is not a relational database…
The analysis highlights Applications and Companies as prominent areas in the source structure around Berkeley DB. 1 topic appears in more than one source area, which can help identify connections that are less obvious in a linear reading.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
The extracted context around Berkeley DB shows recurring relationship patterns in the source. For example, Berkeley DB → After, API, AT, Berkeley, Berkeley DB HA, Berkeley DB's, Berkeley's, BSD, California, CDS, Concurrent Data Store, Data Store, DS, February, HA, High Availability, In, LDAP, Netscape, Netscape's Another extracted example is Berkeley DB → BDB, Bogofilter, Citadel, Citadel/UX, Exim, Linux/Unix, MTA, Notable, Sendmail, Spamassassin, Unix-like. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
berkeley db database software bdb sleepycat oracle open-source license data operating systems version library agpl corporation free system key 2013
TTTA extracted 89 structured relationships around Berkeley DB. Examples in this analysis include Berkeley DB → Developers → Sleepycat Software, later Oracle Corporation and Berkeley DB → License → Dual licensed (GNU Affero General Public License and proprietary license. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Berkeley DB | Developers | Sleepycat Software, later Oracle Corporation | 1.00 | infobox |
| Berkeley DB | License | Dual licensed (GNU Affero General Public License and proprietary license | 1.00 | infobox |
| Berkeley DB | Operating system | Windows, Unix-like | 1.00 | infobox |
| Berkeley DB | Original authors | Margo Seltzer and Keith Bostic of Sleepycat Software | 1.00 | infobox |
| Berkeley DB | Release | 1994; 32 years ago (1994) | 1.00 | infobox |
| Berkeley DB | Size | ~1244 kB compiled on Windows x86 | 1.00 | infobox |
| Berkeley DB | Stable release | 18.1.40 / May 29, 2020; 6 years ago (2020-05-29) | 1.00 | infobox |
| Berkeley DB | Type | Embedded database, NoSQL Database | 1.00 | infobox |
| Berkeley DB | Website | www.oracle.com/database/technologies/related/berkeleydb.html | 1.00 | infobox |
| Berkeley DB | Written in | C | 1.00 | infobox |
| ACID transactions | instance of | The record and its key can both be up to four gigabytes long.Berkeley DB supports database features | 0.80 | text |
| fine-grained locking | instance of | The record and its key can both be up to four gigabytes long.Berkeley DB supports database features | 0.80 | text |
The concept neighborhoods around Berkeley DB bring nearby vocabulary together. In this analysis, examples include Db, Database and Oracle. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Berkeley DB, one of the stronger structural bridges in this analysis connects Berkeley DB with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around Berkeley DB to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Applications & Companies, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Berkeley DB · EN edition · Analysis: TopicsToTalkAbout