A Reference Model for Collaborative Business Intelligence Virtual
Assistants
- URL: http://arxiv.org/abs/2304.10556v1
- Date: Thu, 20 Apr 2023 14:02:21 GMT
- Title: A Reference Model for Collaborative Business Intelligence Virtual
Assistants
- Authors: Olga Cherednichenko (ERIC), Fahad Muhammad (ERIC), J\'er\^ome Darmont
(ERIC), C\'ecile Favre (ERIC, CMW)
- Abstract summary: Collaborative Business Analysis (CBA) is a methodology that involves bringing together different stakeholders to collaboratively analyze data and gain insights into business operations.
CBA typically involves a range of activities, including data gathering and analysis, brainstorming, problem-solving, decision-making and knowledge sharing.
This paper deals with virtual collaboration tools as an important part of Business Intelligence (BI) platform.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Collaborative Business Analysis (CBA) is a methodology that involves bringing
together different stakeholders, including business users, analysts, and
technical specialists, to collaboratively analyze data and gain insights into
business operations. The primary objective of CBA is to encourage knowledge
sharing and collaboration between the different groups involved in business
analysis, as this can lead to a more comprehensive understanding of the data
and better decision-making. CBA typically involves a range of activities,
including data gathering and analysis, brainstorming, problem-solving,
decision-making and knowledge sharing. These activities may take place through
various channels, such as in-person meetings, virtual collaboration tools or
online forums. This paper deals with virtual collaboration tools as an
important part of Business Intelligence (BI) platform. Collaborative Business
Intelligence (CBI) tools are becoming more user-friendly, accessible, and
flexible, allowing users to customize their experience and adapt to their
specific needs. The goal of a virtual assistant is to make data exploration
more accessible to a wider range of users and to reduce the time and effort
required for data analysis. It describes the unified business intelligence
semantic model, coupled with a data warehouse and collaborative unit to employ
data mining technology. Moreover, we propose a virtual assistant for CBI and a
reference model of virtual tools for CBI, which consists of three components:
conversational, data exploration and recommendation agents. We believe that the
allocation of these three functional tasks allows you to structure the CBI
issue and apply relevant and productive models for human-like dialogue,
text-to-command transferring, and recommendations simultaneously. The complex
approach based on these three points gives the basis for virtual tool for
collaboration. CBI encourages people, processes, and technology to enable
everyone sharing and leveraging collective expertise, knowledge and data to
gain valuable insights for making better decisions. This allows to respond more
quickly and effectively to changes in the market or internal operations and
improve the progress.
Related papers
- Capturing and Anticipating User Intents in Data Analytics via Knowledge Graphs [0.061446808540639365]
This work explores the usage of Knowledge Graphs (KG) as a basic framework for capturing a human-centered manner complex analytics.
The data stored in the generated KG can then be exploited to provide assistance (e.g., recommendations) to the users interacting with these systems.
arXiv Detail & Related papers (2024-11-01T20:45:23Z) - Data Analysis in the Era of Generative AI [56.44807642944589]
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges.
We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow.
We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
arXiv Detail & Related papers (2024-09-27T06:31:03Z) - HEMM: Holistic Evaluation of Multimodal Foundation Models [91.60364024897653]
Multimodal foundation models can holistically process text alongside images, video, audio, and other sensory modalities.
It is challenging to characterize and study progress in multimodal foundation models, given the range of possible modeling decisions, tasks, and domains.
arXiv Detail & Related papers (2024-07-03T18:00:48Z) - Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
Real-world systems often incorporate a wide array of tools, making it impractical to input all tools into Large Language Models.
Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions.
We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
arXiv Detail & Related papers (2024-05-25T06:41:23Z) - Collaborative business intelligence virtual assistant [1.9953434933575993]
This study focuses on the applications of data mining within distributed virtual teams through the interaction of users and a CBI Virtual Assistant.
The proposed virtual assistant for CBI endeavors to enhance data exploration accessibility for a wider range of users and streamline the time and effort required for data analysis.
arXiv Detail & Related papers (2023-12-20T05:34:12Z) - Investigating Collaborative Data Practices: a Case Study on Artificial
Intelligence for Healthcare Research [1.3178083420209858]
We look at the collaborative data practices of research consortia tasked with applying AI tools to understand and manage multiple long-term conditions in the UK.
Our findings reveal the adaptation of tools that are used for sharing knowledge and the tailoring of information based on the audience.
We identify meetings as the key setting for facilitating exchanges between disciplines and allowing for the blending and creation of knowledge.
arXiv Detail & Related papers (2023-11-30T10:19:33Z) - AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities
and Challenges [60.56413461109281]
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes.
We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful.
We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
arXiv Detail & Related papers (2023-04-10T15:38:12Z) - OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking
Experience [87.0233567695073]
Existing studies in conversational AI mostly treat task-oriented dialog (TOD) and question answering (QA) as separate tasks.
We propose a new task, Open-Book TOD (OB-TOD), which combines TOD with QA task and expand external knowledge sources.
We propose a unified model OPERA which can appropriately access explicit and implicit external knowledge to tackle the defined task.
arXiv Detail & Related papers (2022-06-24T18:21:26Z) - SemTUI: a Framework for the Interactive Semantic Enrichment of Tabular
Data [0.0]
SemTUI is a framework to make the enrichment process flexible, complete, and effective through the use of semantics.
A task-driven user evaluation proved SemTUI to be understandable, usable, and capable of achieving table enrichment with little effort and time.
arXiv Detail & Related papers (2022-03-17T17:14:21Z) - Augmenting Decision Making via Interactive What-If Analysis [4.920817773181235]
Business users currently need to perform lengthy exploratory analyses.
The increasing complexity of datasets combined with the cognitive limitations of humans makes it challenging to carry over multiple hypotheses.
Here we argue for four functionalities that we believe are necessary to enable business users to interactively learn and reason about the relationships (functions) between sets of data attributes.
arXiv Detail & Related papers (2021-09-13T17:54:30Z) - Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations.
Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
arXiv Detail & Related papers (2021-01-15T10:43:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.