Software service discovery and recommendation help data scientists build scientific workflows - multi-step data analytics procedures - by automating the manual selection of services. Previous research shows that recommending chainable units of work (UoWs), rather than individual services, improves efficiency and reduces data shimming issues. However, UoW recommendation remains an NP-hard problem. To tackle this challenge, this study introduces a novel framework tailored to recommend UoWs in a goal-driven, context-aware manner, thereby facilitating workflow development. The framework is built around layered structure of software service social networks. At its foundation lies a service dependency network, where each edge represents a dependency between a two-service UoW within a specific context. The next layer abstracts each of these edges into a node, with new edges now representing three-service UoWs. This layering process continues iteratively, with subsequent layers capturing increasingly complex UoWs at higher levels of granularity. At high-order layers, UoW nodes are clustered based on their semantic embeddings, with each cluster represented by an intelligent agent. This approach transforms the workflow recommendation problem into a multi-agent collaboration task, where agents work together to identify high-level UoW groupings before refining selections by navigating down the layered structure for finer-grained recommendations. Experimental results over a real-world dataset confirm the effectiveness of the proposed framework in enhancing workflow composition efficiency.

As increasingly more APIs are published on the Internet, effective API recommendation remains a challenge yet highly demanded for mashup developers. This paper formalizes API recommendation as an incremental context-aware recom-mendation problem starting from a set of descriptive words and a set of APIs selected to date, supported by a fine-grained mashup-oriented knowledge graph (MKG). In contrast to traditional knowledge graphs where nodes are coarse-grained entities, entity-and relationship-encapsulated features are extracted as first-class citizens in an MKG, so that implicit feature relationships can be turned into explicit structural relationships. Two models are trained to learn fine-grained API selection strategies through path type patterns in the MKG, starting from intended descriptions and APIs selected, respectively. Extensive experiments over real-world datasets have demonstrated the effectiveness of the method.

As increasingly more software services have been published onto the Internet, it becomes critical yet highly challenging to recommend suitable services to facilitate scientific workflow composition. This paper proposes a novel Natural Language Processing (NLP)-inspired approach to recommending services throughout a workflow development process, based on incrementally learning latent service representation from workflow provenance. A work-flow composition process is formalized as a step-wise, context-aware service selection procedure, which is mapped to next-word prediction in a natural language sentence generation. Historical service dependencies are extracted from workflow provenance to build and enrich a knowledge graph. Each path in the knowledge graph reflects a scenario in a data analytics experiment, which is analogous to a sentence in a conversation. All paths are thus formalized as composable service sequences and are mined, using various patterns, from the established knowledge graph to construct a corpus. Service embeddings are then learned by applying deep learning model from the NLP field. Extensive experiments on the real-world dataset demonstrate the effectiveness and efficiency of the approach.

As service-oriented architecture becoming one prevalent technique to rapidly compose functionalities to customers, increasingly more reusable software components have been published online in the form of web services. To create a mashup, however, it gets not only time-consuming but also error-prone for developers to find suitable services components from such a sea of services. Service discovery and recommendation has thus attracted significant momentum in both academia and industry. This paper proposes a novel incremental recommend-as-you-go approach to recommending next potential service based on the context of a mashup under construction, considering services that have been selected up to the current step as well as the mashup goal. The core technique is an algorithm of learning the embedding of services, which learns their past goal-driven context-aware decision making behaviors in addition to their semantic descriptions and co-occurrence history. A goal exclusionary negative sampling mechanism tailored for mashup development is also developed to improve training performance. Extensive experiments on a real-world dataset demonstrate the effectiveness of this approach.