Current business environment requires new methods that incorporate more intelligent technologies and tools capable to provide fast, accurate and reliable information for decision making. This paper deals with data mining applications. It describes the unified business intelligence semantic model, coupled with a data warehouse and collaborative unit to employ data mining technology. The virtual assistant for collaborative business intelligence is suggested.
Learning
Ukraine
2023 Copyright for this paper by its authors.
Data exploration is an important component of the BI process, which involves collecting, identifying, and analyzing data to discover meaningful insights and patterns. The main goal of data exploration is to identify key business opportunities and challenges that can drive decision-making and improve business performance.
The main idea of our research is to model CBI processes in distributed virtual teams via interaction of user and CBI Virtual Assistant (Fig. 1).
Results Command
Execution Collect and Keep
Results
User User s Request
Questions /
Comments Request Identification
Request Classification
User s Answer Command Identification to interact with data and extract insights, making decision-making processes more efficient and effective. Natural Language Querying (NLQ) can also make it easier for non-technical users to access and analyze data.
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 is an idea of creating innovative CAs is to transform the way users interact with data and ML models and to make data science more accessible to a wider range of users.
Chatbots can be classified into different categories based on their functionalities and the type
of collaboration they facilitate [
Although a chatbot is a type of conversational agent (CA), not all CAs are chatbots. CA is
a broader term that includes any computer program or system that can engage in natural
language interactions with users [
Drawing upon a review of 233,085 papers, the authors of [
We propose to consider the following main stages of the research. First, a domain must be defined in which collaborative analysis and BI can be modeled. Considering different goals, preferences, experience and conditions, different users will access the same data with different requests forming the content of collaborative session. Second, data sources need to be identified. Combining data from different sources requires solving the problems of data consolidation, cleaning, and standardization. Thirdly, one of the main stages is the formation of a knowledge base of collaborative decision-making cases. At this stage, you need to develop an information model for collecting data about each session, including user behavior and the results of his research, as well as interaction with other users. Fourth, it is necessary to develop a convenient interface for visualizing data and organizing interaction in the virtual space. The final stage is associated with the processing, analysis, and summarizing of the collected data about user behavior. We believe that as a result we will be able to create a CBI framework and prove models and technologies for supporting virtual space, which will expand the functionality of the BI4people project platform.
Let us describe the data we use for experimenting. For each bodily accident occurring on a
road open to public traffic, involving at least one vehicle and causing at least one victim
requiring treatment, information describing the accident is entered by the law enforcement unit
(police, gendarmerie, etc.) which intervened at the scene of the accident [
The databases, extracted from the BAAC file, list all the bodily injury accidents occurring
during a specific year in mainland France and in the overseas departments with a simplified
description. This includes accident location information, as entered, as well as information
regarding the characteristics of the accident and its location, the vehicles involved and their
victims. Every year, road accidents cause thousands of deaths. People are wondering what the
causes are, what specific issues influence the most, who are under the risk etc. We use the
dataset available at [
allocated to the circulation of vehicles does not include hard shoulders, TPCs and parking spaces (in m).
not specified not specified -1 – Not specified 1 – One way
-1 – Not filled in 0 – Not applicable 1 – Cycle path 2 – Cycle lane 3 – Reserved lane - 1 – Not specified 1 – Flat 2 – Slope 3 – Top of hill 4 – Bottom of hill -1 – Not filled in 1 – Straight part 2 – Curved left 3 – Curved right 4 – “S” shaped not specified not specified -1 – Not specified 1 – Normal 2 – Wet 3 – Puddles
6 – Mud 7 – Icy 8 – Fats – oil infra
-1 – Not filled in 0 – None
Railway 5 – Developed crossroads 6 – Pedestrian zone 7 – Toll area 8 – Construction site 9 – Others -1 – Not specified 0 – None 1 – On the road 2 – On hard shoulder
Possible values
not specified the seat occupied in the vehicle by the user at the time of the accident (detail is given by the illustration) 10 – Pedestrian (not applicable) 1 – Driver 2 – Passenger 3 – Pedestrian 1 – Male 2 – Feminine -1 – Not specified 0 – Not filled in 1 – Home – work 2 – Home – school 3 – Shopping – purchases 4 – Professional use 5 – Walk – leisure
-1 – Not filled in 0 – No equipment 1 – Belt
num_veh
possible to specify whether the injured pedestrian was alone
1 – PK or PR or ascending mailing address number 2 – PK or PR or descending postal address number 3 – No mark 00 – Indeterminable 01 – Bicycle 02 – Moped <50cm3 03 – Cart (Quadricycle with bodied motor) (formerly "cart or motor tricycle") 04 – Reference unused since 2006 (registered scooter) 05 – Unused reference since 2006 (motorcycle) 06 – Reference unused since 2006 (sidecar) 07 – LV only 08 – Reference unused since 2006 (VL + caravan) 09 – Reference unused since 2006 (VL + trailer) 10 – LCV only 1.5T <= GVW <= 3.5T with or without trailer (formerly LCV only 1.5T <= GVW<= 3.5T) 11 – Reference unused since 2006 (VU (10) + caravan) 12 – Reference unused since 2006 (VU (10) + trailer) 13 – PL only 3.5T <PTCA <= 7.5T 14 – PL only > 7.5T 15 – HGV > 3.5T + trailer 16 – Road tractor alone 17 – Road tractor + semi-trailer 18 – Reference unused since 2006 (public transport) 19 – Reference unused since 2006 (tramway) 20 – Special gear 21 – Agricultural tractor 30 – Scooter < 50 cc 31 – Motorcycle > 50 cm3 and <= 125 cm3 32 – Scooter > 50 cm3 and <= 125 cm3 33 – Motorcycle > 125 cm3 34 – Scooter > 125 cc 35 – Light quad <= 50 cc (Unbodied motor quadricycle) 36 – Heavy quad > 50 cm3 (Quadricycle with motor without bodywork) 37 – Buses occutc obs
Int 38 – Bus 39 – Train 40 – Tramway 41 – 3WD <= 50cc 42 – 3WD > 50cc <= 125cc 43 – 3WD > 125 cc 50 – Motor EDP 60 – EDP without engine 80 – VAE 99 – Other vehicle
not specified -1 – Not specified 0 – Not applicable 1 – Parked vehicle 2 – Tree 3 – Metal slider 4 – Concrete slide 5 – Other slide 6 – Building, wall, bridge pier 7 – Vertical signaling support or emergency call station 8 – Pos 9 – Street furniture 10 – Parapet 11 – Island, refuge, high boundary 12 – Sidewalk curb 13 – Ditch, embankment, rock face 14 – Other fixed obstacle on roadway 15 – Other fixed obstacle on sidewalk or shoulder 16 – Obstacle-free road exit 17 – Nozzle – aqueduct head -1 – Not specified 0 – None 1 – Pedestrian 2 – Vehicle 4 – Rail vehicle 5 – Domestic animal 6 – Wild animal 9 – Other -1 – Not specified 0 – None 1 – Before 2 – Front right 3 – Front left 4 – Back
vehicle
Str
Standard metadata formats exist to facilitate their collection, search and automatic processing. The retained metadata are as follows: • Title • Acronym • Description • Licence • Update frequency • Key words • Temporal coverage • Spatial coverage • Spatial granularity • Private mode
On the one hand, data reusers struggle to identify quality datasets and to assess whether such and such a dataset is worthy of interest. On the other hand, data producers are not sufficiently encouraged and supported to improve the quality of their data. It is set up a metadata quality score on data.gouv.fr.
The table 5 is depicts the metadata used.
the data set is sufficiently long). - The update frequency is entered. - The update frequency is respected - The license is populated.
- The license is open
In 2015, when the town hall of Paris launched its Cycling Plan, it could not have imagined that the end of it would coincide with a health context favoring its utilitarian practice. If the first plan was ambitious in its redevelopment of the city's cycle paths, the balance sheet of bicycle accidents in Paris and the reasons relating to it question the effectiveness of the first plan.
The advent of the health crisis in 2020 relating to the Covid-19 pandemic favors cycling but requires these recent developments to be maintained and expanded. The appearance of the socalled "coronapists" with the aim of improving traffic flow and relieving public transport has initiated many new cyclists. If many European cities like Saint-Etienne or Marseille decide to erase them after a few weeks, the town hall of Paris obtains the agreement of the government that they are supported in the plan “France Relance”. 2020 is becoming the year of the bike. The culture of utilitarian cycling is anchored in the daily lives of many Parisians, questioning their safety.
The proportion of accident victims wearing a helmet is also the majority and raises the question of prevention and risky behavior adopted by cyclists in Paris. Is it due to an infrastructure problem that supports the idea that the roads are not safe enough, even for users aware of the risks? How effective are city hall's prevention efforts? The data does not allow us to determine the causes of the accidentology, nevertheless they shed light on persistent problems.
Based on the analysis done, there are several important features that must be implemented in virtual assistant software in order to assist novice buyers effectively. These features include: • The ability to perform various data exploration commands such as filtering, querying, selecting, and setting parameters. • An information retrieval module is necessary to find relevant information, explore options, and research user needs. • Item matching is necessary to compare different offers and proposals. • Personalization based on user preferences, history, and behavior, which can enhance the relevance and effectiveness of the recommendations provided by the assistant. • Machine learning algorithms to continuously learn from user interactions and improve the assistant's ability to provide personalized recommendations. • Language understanding and text generation are both necessary in order to effectively communicate with the user.
In this paper we have presented an approach how to create, approbate and estimate collaborative decision-making models. BI systems are vastly used as a tool to support decisionmaking in different kind of organizations. CBI give even more opportunities for reasonable decision-making as they allow using external information from various sources. We are collecting and processing data, developing convenient interface and tools for collaborative analysis. The next step is to implement a prototype
The research study depicted in this paper is funded by the French National Research Agency (ANR), project ANR-19-CE23-0005 BI4people (Business intelligence for the people). 8. References