An evaluation of Protocol Buffer

Kaur, Gurpreet; Fuad, Muztaba

doi:10.1109/secon.2010.5453828

Cited by 20 publications

(6 citation statements)

References 1 publication

(1 reference statement)

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“…It is important to remember that JSON documents, like XML documents, are divided into structure and data parts, hence the proposed method is equally applicable and effective on JSON documents as well. Recently, a new encoding method, the Google Protocol Buffer [25], was introduced. This can be used to represent messages in a format that is shorter than JSON.…”

Section: H Discussionmentioning

confidence: 99%

Compression of XML and JSON API Responses

2021

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Web services are the de-facto standard for implementing web-based systems today, and comprise message-based interactions involving XML and JSON documents. These formats can be quite verbose, especially XML, and therefore compression of such documents can potentially improve the communication efficiency and performance of service-oriented systems. In this paper, we review the various formulations of XML compression and propose a novel technique for the same, wherein large section of the documents are substituted by numerical representations. The approach is simple yet effective, especially on small documents that constitute the bulk of communicated content in web-based systems. We conduct experiments with several datasets and demonstrate that the proposed technique for XML compression outperforms the existing stateof-the-art techniques. INDEX TERMSCompression of Web API responses, compression of ReSTful responses, JSON compression, XML compression.

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Section: H Discussionmentioning

confidence: 99%

Compression of XML and JSON API Responses

2021

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“…The rule computing layer adopts C++ language to realize high concurrency. The Rpc communication between the rule parsing layer, rule computing layer, and rule pushing layer is carried out through the Thrift framework of Apache [9], and the data transmission Protocol is the Protocol Buffer of Google [10]. The overall process is shown in Figure 5.…”

Section: Architecture Design Of Ride-hailing Rule Engine Systemmentioning

confidence: 99%

Rule engine system for ride-hailing services based on bitmap algorithm

2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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Online car-hailing is now an essential mode of transportation for people to travel. The rule engine system plays an important role in the matching mechanism of online car-hailing orders and drivers. The existing network car-hailing rule engine system is mostly implemented in the form of manually written configuration files. However, manual handwritten configuration files are prone to errors, and the efficiency of manually writing rule configuration files is low. This method needs to be faster to calculate rule matching in high-concurrency scenarios. This paper proposes a rule engine system based on the bitmap algorithm and inverted index mechanism. Different rules are divided into corresponding rule templates at the rule configuration level according to the practical dimension, which users can automatically generate rule expressions according to the templates. The system reduces the difficulty of user configuration rules, increases the efficiency of operation rules, reduces the time-consuming of rule matching, and completely releases the productivity of engineers.

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“…Faithfulness and brevity have been studied in both software engineering and LLM contexts and have implications for maintenance costs and usability (Ahn et al, 2022;Attanasio et al, 2022;Flauzino et al, 2018;Kaur and Fuad, 2010). There are many other design dimensions that could be evaluated in future research.…”

Section: Api Design Choicesmentioning

confidence: 99%

Synthetic APIs: Enabling Language Models to Act as Interlocutors Between Natural Language and Code

Mullins,

Terry

2023

Proceedings of the Annual Hawaii International Conference on System Sciences

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Large language models (LLMs) can synthesize code from natural language descriptions or by completing code in-context. In this paper, we consider the ability of LLMs to synthesize code, at inference time, for a novel API not in its training data, and specifically examine the impact of different API designs on this ability. We find that: 1) code examples in model training data seem to facilitate API use at inference time; 2) hallucination is the most common failure mode; and 3) the designs of both the novel API and the prompt affect performance. In light of these findings, we introduce the concept of a Synthetic API: an API designed to be used by LLMs instead of by humans. Synthetic APIs for LLMs offer the potential to further accelerate development of natural language interfaces to arbitrary tools and services.

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An evaluation of Protocol Buffer

Cited by 20 publications

References 1 publication

Compression of XML and JSON API Responses

Compression of XML and JSON API Responses

Rule engine system for ride-hailing services based on bitmap algorithm

Synthetic APIs: Enabling Language Models to Act as Interlocutors Between Natural Language and Code

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