scalar-labs · josh-wong · Dec 13, 2024 · Dec 13, 2024
diff --git a/docs/design.mdx b/docs/design.mdx
@@ -34,11 +34,15 @@ The key to the Byzantine fault detection protocol of ScalarDL is that Ledger (pr
 
 The three-phase protocol makes both databases derive the same correct (strict serializable) states and results as long as both Ledger and Auditor are honest. If either is Byzantine faulty, for example, the records have been tampered, their states or results would diverge. When the divergence is observable outside of the database system, correct ScalarDL clients can detect it. In other words, ScalarDL detects a Byzantine fault only when the clients observe the divergence in the response from the database system. Therefore, ScalarDL does not detect the fault instantly, but it does guarantee that the clients will detect the fault when the diverged states are about to be used, minimizing the validation overheads.
 
-## Learn more
+## Further reading
 
-For details about the design and implementation of ScalarDL, please see the following materials, which Scalar presented at the VLDB 2022 conference:
+For more details about the design and implementation of ScalarDL, see the following documents:
 
-- **Speakerdeck presentation:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://speakerdeck.com/scalar/scalar-dl-scalable-and-practical-byzantine-fault-detection-for-transactional-database-systems-vldb22)
+- **Speaker Deck presentation:** [ScalarDL Technical Overview](https://speakerdeck.com/scalar/scalar-dl-technical-overview)
+
+In addition, the following materials were presented at the VLDB 2022 conference:
+
+- **Speaker Deck presentation:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://speakerdeck.com/scalar/scalar-dl-scalable-and-practical-byzantine-fault-detection-for-transactional-database-systems-vldb22)
 - **Detailed paper:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://www.vldb.org/pvldb/vol15/p1324-yamada.pdf)
 
 [^1]: Leslie Lamport, Robert Shostak, Marshall Pease, The Byzantine Generals Problem, ACM Transactions on Programming Languages and Systems (TOPLAS), v.4 n.3, p.382-401, July 1982.
diff --git a/i18n/versioned_docs/ja-jp/docusaurus-plugin-content-docs/current/design.mdx b/i18n/versioned_docs/ja-jp/docusaurus-plugin-content-docs/current/design.mdx
@@ -38,11 +38,15 @@ ScalarDL のビザンチン故障検出プロトコルの鍵は、Ledger (プラ
 
 この3フェーズプロトコルでは、Ledger と Auditor の両方が正常である限り、両方のデータベースが同じ正しい (厳密に直列化可能な) 状態と結果を導き出します。どちらかがビザンチン故障を起こした場合 (たとえば、レコードを改ざんした場合)、その状態または結果は乖離します。データベースシステムの外部でこの乖離が観察できる場合、正しい ScalarDL クライアントはそれを検出できます。言い換えると、ScalarDL は、クライアントがデータベースシステムからの応答で乖離を観察した場合にのみ、ビザンチン故障を検出します。そのため、ScalarDLは故障を即座に検出することはできませんが、検証オーバーヘッドを削減しつつ、乖離した状態や結果を使用しようとした際に故障が検出されることを保証します。
 
-## 詳細情報
+## 詳細
 
-ScalarDL の設計と実装の詳細については、Scalar が VLDB 2022 カンファレンスで発表した次の資料を参照してください。
+ScalarDL の設計と実装の詳細については、次のドキュメントを参照してください。
 
-- **VLDB'22 での発表資料:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://speakerdeck.com/scalar/scalar-dl-scalable-and-practical-byzantine-fault-detection-for-transactional-database-systems-vldb22)
-- **ホワイトペーパー (VLDB'22 の論文):** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://www.vldb.org/pvldb/vol15/p1324-yamada.pdf)
+- **Speaker Deck プレゼンテーション:** [ScalarDL Technical Overview](https://speakerdeck.com/scalar/scalar-dl-technical-overview)
+
+さらに、VLDB 2022 カンファレンスでは以下の資料が発表されました。
+
+- **Speaker Deck プレゼンテーション:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://speakerdeck.com/scalar/scalar-dl-scalable-and-practical-byzantine-fault-detection-for-transactional-database-systems-vldb22)
+- **詳細な論文:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://www.vldb.org/pvldb/vol15/p1324-yamada.pdf)
 
 [^1]: Leslie Lamport, Robert Shostak, Marshall Pease, The Byzantine Generals Problem, ACM Transactions on Programming Languages and Systems (TOPLAS), v.4 n.3, p.382-401, July 1982.
diff --git a/i18n/versioned_docs/ja-jp/docusaurus-plugin-content-docs/version-3.8/design.mdx b/i18n/versioned_docs/ja-jp/docusaurus-plugin-content-docs/version-3.8/design.mdx
@@ -38,11 +38,15 @@ ScalarDL のビザンチン故障検出プロトコルの鍵は、Ledger (プラ
 
 この3フェーズプロトコルでは、Ledger と Auditor の両方が正常である限り、両方のデータベースが同じ正しい (厳密に直列化可能な) 状態と結果を導き出します。どちらかがビザンチン故障を起こした場合 (たとえば、レコードを改ざんした場合)、その状態または結果は乖離します。データベースシステムの外部でこの乖離が観察できる場合、正しい ScalarDL クライアントはそれを検出できます。言い換えると、ScalarDL は、クライアントがデータベースシステムからの応答で乖離を観察した場合にのみ、ビザンチン故障を検出します。そのため、ScalarDLは故障を即座に検出することはできませんが、検証オーバーヘッドを削減しつつ、乖離した状態や結果を使用しようとした際に故障が検出されることを保証します。
 
-## 詳細情報
+## 詳細
 
-ScalarDL の設計と実装の詳細については、Scalar が VLDB 2022 カンファレンスで発表した次の資料を参照してください。
+ScalarDL の設計と実装の詳細については、次のドキュメントを参照してください。
 
-- **VLDB'22 での発表資料:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://speakerdeck.com/scalar/scalar-dl-scalable-and-practical-byzantine-fault-detection-for-transactional-database-systems-vldb22)
-- **ホワイトペーパー (VLDB'22 の論文):** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://www.vldb.org/pvldb/vol15/p1324-yamada.pdf)
+- **Speaker Deck プレゼンテーション:** [ScalarDL Technical Overview](https://speakerdeck.com/scalar/scalar-dl-technical-overview)
+
+さらに、VLDB 2022 カンファレンスでは以下の資料が発表されました。
+
+- **Speaker Deck プレゼンテーション:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://speakerdeck.com/scalar/scalar-dl-scalable-and-practical-byzantine-fault-detection-for-transactional-database-systems-vldb22)
+- **詳細な論文:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://www.vldb.org/pvldb/vol15/p1324-yamada.pdf)
 
 [^1]: Leslie Lamport, Robert Shostak, Marshall Pease, The Byzantine Generals Problem, ACM Transactions on Programming Languages and Systems (TOPLAS), v.4 n.3, p.382-401, July 1982.
diff --git a/i18n/versioned_docs/ja-jp/docusaurus-plugin-content-docs/version-3.9/design.mdx b/i18n/versioned_docs/ja-jp/docusaurus-plugin-content-docs/version-3.9/design.mdx
@@ -38,11 +38,15 @@ ScalarDL のビザンチン故障検出プロトコルの鍵は、Ledger (プラ
 
 この3フェーズプロトコルでは、Ledger と Auditor の両方が正常である限り、両方のデータベースが同じ正しい (厳密に直列化可能な) 状態と結果を導き出します。どちらかがビザンチン故障を起こした場合 (たとえば、レコードを改ざんした場合)、その状態または結果は乖離します。データベースシステムの外部でこの乖離が観察できる場合、正しい ScalarDL クライアントはそれを検出できます。言い換えると、ScalarDL は、クライアントがデータベースシステムからの応答で乖離を観察した場合にのみ、ビザンチン故障を検出します。そのため、ScalarDLは故障を即座に検出することはできませんが、検証オーバーヘッドを削減しつつ、乖離した状態や結果を使用しようとした際に故障が検出されることを保証します。
 
-## 詳細情報
+## 詳細
 
-ScalarDL の設計と実装の詳細については、Scalar が VLDB 2022 カンファレンスで発表した次の資料を参照してください。
+ScalarDL の設計と実装の詳細については、次のドキュメントを参照してください。
 
-- **VLDB'22 での発表資料:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://speakerdeck.com/scalar/scalar-dl-scalable-and-practical-byzantine-fault-detection-for-transactional-database-systems-vldb22)
-- **ホワイトペーパー (VLDB'22 の論文):** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://www.vldb.org/pvldb/vol15/p1324-yamada.pdf)
+- **Speaker Deck プレゼンテーション:** [ScalarDL Technical Overview](https://speakerdeck.com/scalar/scalar-dl-technical-overview)
+
+さらに、VLDB 2022 カンファレンスでは以下の資料が発表されました。
+
+- **Speaker Deck プレゼンテーション:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://speakerdeck.com/scalar/scalar-dl-scalable-and-practical-byzantine-fault-detection-for-transactional-database-systems-vldb22)
+- **詳細な論文:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://www.vldb.org/pvldb/vol15/p1324-yamada.pdf)
 
 [^1]: Leslie Lamport, Robert Shostak, Marshall Pease, The Byzantine Generals Problem, ACM Transactions on Programming Languages and Systems (TOPLAS), v.4 n.3, p.382-401, July 1982.
diff --git a/versioned_docs/version-3.8/design.mdx b/versioned_docs/version-3.8/design.mdx
@@ -34,11 +34,15 @@ The key to the Byzantine fault detection protocol of ScalarDL is that Ledger (pr
 
 The three-phase protocol makes both databases derive the same correct (strict serializable) states and results as long as both Ledger and Auditor are honest. If either is Byzantine faulty, for example, the records have been tampered, their states or results would diverge. When the divergence is observable outside of the database system, correct ScalarDL clients can detect it. In other words, ScalarDL detects a Byzantine fault only when the clients observe the divergence in the response from the database system. Therefore, ScalarDL does not detect the fault instantly, but it does guarantee that the clients will detect the fault when the diverged states are about to be used, minimizing the validation overheads.
 
-## Learn more
+## Further reading
 
-For details about the design and implementation of ScalarDL, please see the following materials, which Scalar presented at the VLDB 2022 conference:
+For more details about the design and implementation of ScalarDL, see the following documents:
 
-- **Speakerdeck presentation:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://speakerdeck.com/scalar/scalar-dl-scalable-and-practical-byzantine-fault-detection-for-transactional-database-systems-vldb22)
+- **Speaker Deck presentation:** [ScalarDL Technical Overview](https://speakerdeck.com/scalar/scalar-dl-technical-overview)
+
+In addition, the following materials were presented at the VLDB 2022 conference:
+
+- **Speaker Deck presentation:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://speakerdeck.com/scalar/scalar-dl-scalable-and-practical-byzantine-fault-detection-for-transactional-database-systems-vldb22)
 - **Detailed paper:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://www.vldb.org/pvldb/vol15/p1324-yamada.pdf)
 
 [^1]: Leslie Lamport, Robert Shostak, Marshall Pease, The Byzantine Generals Problem, ACM Transactions on Programming Languages and Systems (TOPLAS), v.4 n.3, p.382-401, July 1982.
diff --git a/versioned_docs/version-3.9/design.mdx b/versioned_docs/version-3.9/design.mdx
@@ -34,11 +34,15 @@ The key to the Byzantine fault detection protocol of ScalarDL is that Ledger (pr
 
 The three-phase protocol makes both databases derive the same correct (strict serializable) states and results as long as both Ledger and Auditor are honest. If either is Byzantine faulty, for example, the records have been tampered, their states or results would diverge. When the divergence is observable outside of the database system, correct ScalarDL clients can detect it. In other words, ScalarDL detects a Byzantine fault only when the clients observe the divergence in the response from the database system. Therefore, ScalarDL does not detect the fault instantly, but it does guarantee that the clients will detect the fault when the diverged states are about to be used, minimizing the validation overheads.
 
-## Learn more
+## Further reading
 
-For details about the design and implementation of ScalarDL, please see the following materials, which Scalar presented at the VLDB 2022 conference:
+For more details about the design and implementation of ScalarDL, see the following documents:
 
-- **Speakerdeck presentation:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://speakerdeck.com/scalar/scalar-dl-scalable-and-practical-byzantine-fault-detection-for-transactional-database-systems-vldb22)
+- **Speaker Deck presentation:** [ScalarDL Technical Overview](https://speakerdeck.com/scalar/scalar-dl-technical-overview)
+
+In addition, the following materials were presented at the VLDB 2022 conference:
+
+- **Speaker Deck presentation:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://speakerdeck.com/scalar/scalar-dl-scalable-and-practical-byzantine-fault-detection-for-transactional-database-systems-vldb22)
 - **Detailed paper:** [ScalarDL: Scalable and Practical Byzantine Fault Detection for Transactional Database Systems](https://www.vldb.org/pvldb/vol15/p1324-yamada.pdf)
 
 [^1]: Leslie Lamport, Robert Shostak, Marshall Pease, The Byzantine Generals Problem, ACM Transactions on Programming Languages and Systems (TOPLAS), v.4 n.3, p.382-401, July 1982.