Graph data is becoming more and more pervasive. Indeed, services such as Social Networks or the Semantic Web can no longer rely on the traditional relational model, as its structure is somewhat too rigid for the applications they have in mind. For this reason we have seen a continuous shift towards more non-standard models. First it was the semi-structured data in the 1990s and XML in 2000s, but even such models seem to be too restrictive for new applications that require navigational properties naturally modelled by graphs. Social networks fit into the graph model by their very design: users are nodes and their connections are specified by graph edges. The W3C committee, on the other hand, describes RDF, the model underlying the Semantic Web, by using graphs. The situation is quite similar with crime detection networks and tracking workflow provenance, namely they all have graphs inbuilt into their definition. With pervasiveness of graph data the important question of querying and maintaining it has emerged as one of the main priorities, both in theoretical and applied sense. Currently there seem to be two approaches to handling such data. On the one hand, to extract the actual data, practitioners use traditional relational languages that completely disregard various navigational patterns connecting the data. What makes this data interesting in modern applications, however, is precisely its ability to compactly represent intricate topological properties that envelop the data. To overcome this issue several languages that allow querying graph topology have been proposed and extensively studied. The problem with these languages is that they concentrate on navigation only, thus disregarding the data that is actually stored in the database. What we propose in this thesis is the ability to do both. Namely, we will study how query languages can be designed to allow specifying not only how the data is connected, but also how data changes along paths and patterns connecting it. To this end we will develop several query languages and show how adding different data manipulation capabilities and different navigational features affects the complexity of main reasoning tasks. The story here is somewhat similar to the early success of the relational data model, where theoretical considerations led to a better understanding of what makes certain tasks more challenging than others. Here we aim for languages that are both efficient and capable of expressing a wide variety of queries of interest to several groups of practitioners. To do so we will analyse how different requirements affect the language at hand and at the end provide a good base of primitives whose inclusion into a language should be considered, based on the applications one has in mind. Namely, we consider how adding a specific operation, mechanism, or capability to the language affects practical tasks that such an addition plans to tackle. In the end we arrive at several languages, all of them with their pros and cons, giving us a good overview of how specific capabilities of the language affect the design goals, thus providing a sound basis for practitioners to choose from, based on their requirements.

abstract: In today's data-driven world, every datum is connected to a large amount of data. Relational databases have been proving itself a pioneer in the field of data storage and manipulation since 1970s. But more recently they have been challenged by NoSQL graph databases in handling data models which have an inherent graphical representation. Graph databases with the ability to store physical relationships between two nodes and native graph processing technique have been doing exceptionally well in graph data storage and management for applications like recommendation engines, biological modeling, network modeling, social media applications, etc. Instructional Module Development System (IMODS) is a web-based software system that guides STEM instructors through the complex task of curriculum design, ensures tight alignment between various components of a course (i.e., learning objectives, content, assessments), and provides relevant information about research-based pedagogical and assessment strategies. The data model of IMODS is highly connected and has an inherent graphical representation between all its entities with numerous relationships between them. This thesis focuses on developing an algorithm to determine completeness of course design developed using IMODS. As part of this research objective, the study also analyzes the data model for best fit database to run these algorithms. As part of this thesis, two separate applications abstracting the data model of IMODS have been developed - one with Neo4j (graph database) and another with PostgreSQL (relational database). The research objectives of the thesis are as follows: (i) evaluate the performance of Neo4j and PostgreSQL in handling complex queries that will be fired throughout the life cycle of the course design process; (ii) devise an algorithm to determine the completeness of a course design developed using IMODS. This thesis presents the process of creating data model for PostgreSQL and converting it into a graph data model to be abstracted by Neo4j, creating SQL and CYPHER scripts for undertaking experiments on both platforms, testing and elaborate analysis of the results and evaluation of the databases in the context of IMODS. / Dissertation/Thesis / Masters Thesis Computer Science 2017

The internet is larger than ever and so is the amount of information on the internet.The average user on the internet has next to endless possibilities and choices whichcan cause information overload. Companies have therefore developed systems toguide their users to find the right product or object in the form of recommendersystems. Recommender systems are tools created to filter data and find patternsto recommend relevant information for specific customers with the help of differentalgorithms. MarketHype is a company that aggregates large amounts of data aboutevent organizers, their events, their visitors, and related transactions. They want inthe near future to be able to manage and offer event organizers recommended targetgroups for their events using a recommender system.This study tries to find a solution on how to model event data in a graph databaseto support relevant recommendations for event organizers. The method used to answer the question was an empirical research method. The goal was to create aprototype of a recommender system with help of event data. The main focus was tomodel a graph database in the software Neo4j that can be used for finding recommendations with different Cypher queries. A literature study was later conducted tofind what advantages and disadvantages a graph database could have on event data.This information could then answer how further development of the system couldwork.The result was a system that was implemented with the help of data from fourdifferent CSV files. The data provided were information about contacts, persons,orders, and events. This information was used to create the nodes and relationships.A total of 4.4 million nodes were created and around 5 million relationships betweenthose nodes. Collaborative and content-based filtering was the main recommendationtechnique used in order to find the best-suitable recommendations. This was donewith different queries in Cypher.The main conclusion is that a graph database in Neo4j is a good method in orderto implement a recommender system with event data. The result shows that thecollaborative filtering approach is a major factor in the system’s success in findingrelevant information. The approach of letting other contacts decide what the originalcontract wants is proven to work well with event data. The result also states thatthe recommendation is more of an indication because it returns what supposedlywould be the preferences for a contact. A solution for a better recommender systemwas found which includes another layer to the content-based filtering in the form ofcategorized events.

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As society becomes more data-driven, new alternative technologies to the more traditional relational database model arise. One of these technologies is the graph database model, which stores the data as nodes and edges, where the edges define the relationships between the nodes representing entities. Graph databases are a great fit for data with dense and dynamic relationships, such as social networks, fraud detection and recommendation engines. At the Swedish Pensions Agency, the unit working with fraud detection has rapidly increased personnel due to increased demand for fraud prevention. New technologies are being investigated to improve the efficiency in both detection of current fraudulent activities and also to detect suspicious activities and persons to prevent fraud from even happening. The scope of this thesis is to develop a tool as a proof of concept to import data through the web browser into a graph database, namely Neo4j. The evaluations show that importing nodes and relationships with the tool is slower than importing with Neo4j LOAD CSV. However, the performance is still reasonable for data sets of a few hundred thousand nodes or relationships. The evaluations with the Swedish Pensions Agency show that the tool could bring value to them by removing the complexity of writing code.

Java is one of the most widely used programming languages used today. CSN that previously used 4 GL tool should now migrate to Java development, which means that there is a need for a tool to show dependencies and relationships in the migrated and newly developed Java code. The GuardIEn was previously used in the old code base, but that tool will be wound up after CSN's after migration to Java. The overall purpose of the project is to create a graph database with data that is scanned with the tool jqAssistant. This database is then used by a Java backend that retrieves relationships and nodes from the graph database which is then used with a separate web interface in Angular to visualize all relations between program code / Java är en av de mest använda programmeringsspråken som används idag. CSN som tidigare använt 4 GL verktyg skall nu migrera till Java-utveckling vilket innebär att det finns ett behov av ett verktyg för att visa beroenden och relationer i den migrerade och nyutvecklade Java-koden. GuardIEn användes innan för detta i den gamla kodbasen men det verktyget kommer avvecklas efter CSN:s efter migrering till Java. Projektets övergripande syfte är att skapa en grafdatabas med data som skannas in med verktyget jqAssistant. Denna databas används sedan av en backend applikation som hämtar relationer och noder från grafdatabasen som sedan används med ett eget webbgränssnitt i Angular för visualisera alla relationer mellan programkod. Det har också undersökts kring funktioner på att söka efter programkod och filnamn i kodbasen för att hitta och kunna visa källkoden.

Um indicador importante na área acadêmica está relacionado ao grau de impacto de uma publicação, o que pode auxiliar na avaliação da qualidade e do grau de internacionalização de uma instituição. Para melhor delimitar esse indicador torna-se necessária a realização de uma análise das redes de colaboração dos autores envolvidos. Considerando que o modelo de dados relacional é o modelo predominante dos bancos de dados atuais, observa-se que a análise das redes de colaboração é prejudicada pelo fato desse modelo não atender, com o mesmo desempenho, a todos os tipos de consultas realizadas. Uma alternativa para executar as consultas que perdem desempenho no modelo de banco de dados relacional é a utilização do modelo de banco de dados orientado a grafos. Porém, não é claro quais parâmetros podem ser utilizados para definir quando utilizar cada um dos modelos de bancos de dados. Assim, este trabalho tem como objetivo fazer uma análise de consultas que, a partir da sintaxe da consulta e do ambiente de execução, possa apontar o modelo de dados mais adequado para execução da referida consulta. Com essa análise, é possível delimitar em que cenários uma integração entre o modelo relacional e o orientado a grafos é mais adequada. / An important indicator in the academic area is related to the degree of impact of a publication that can help in evaluating the quality and degree of internationalization in academic institutions. One approach to better understand the aforementioned indicator is analyzing the collaboration network formed by each researcher. In order to analyze this network, several alternatives use the well known relational data model which is predominant in most databases used today. Even though this model is widely used, it has a performance drawback when some types of queries are performed. For overcoming this drawback, certain alternatives are using a graph-oriented database model which is similar to a collaboration network model. However, it is unclear what parameters can be used to define when to use a relational or graph-oriented model. In this work, we propose an analysis of queries that, from the syntax of a query and the execution environment, can point to the most suitable data model for the execution given a specific query. With this query analysis, it is possible to delimit in which scenarios an integration between the relational and the graph-oriented models is more appropriate.

Chatbots are computer programs with the capability to lead a conversation with a human user. When a chatbot is unable to match a user’s utterance to any predefined answer, it will use a fallback intent; a generic response that does not contribute to the conversation in any meaningful way. This report aims to investigate if a content-based recommender system could provide support to a chatbot agent in case of these fallback experiences. Content-based recommender systems use content to filter, prioritize and deliver relevant information to users. Their purpose is to search through a large amount of content and predict recommendations based on user requirements. The recommender system developed in this project consists of four components: a web spider, a Bag-of-words model, a graph database, and the GraphQL API. The anticipation was to capture web page articles and rank them with a numeric scoring to figure out which articles that make for the best recommendation concerning given subjects. The chatbot agent could then use these recommended articles to provide the user with value and help instead of a generic response. After the evaluation, it was found that the recommender system in principle fulfilled all requirements, but that the scoring algorithm used could achieve significant improvements in its recommendations if a more advanced algorithm would be implemented. The scoring algorithm used in this project is based on word count, which lacks taking the context of the dialogue between the user and the agent into consideration, among other things.

Graphs that evolve over time are called temporal graphs. They can be used to describe and represent real-world networks, including transportation networks, social networks, and communication networks, with higher fidelity and accuracy. However, research is still limited on how to manage large scale temporal graphs and execute queries over these graphs efficiently and effectively. This thesis investigates the problems of temporal graph data management related to node and edge sequence queries. In temporal graphs, nodes and edges can evolve over time. Therefore, sequence queries on nodes and edges can be key components in managing temporal graphs. In this thesis, the node sequence query decomposes into two parts: graph node similarity and subsequence matching. For node similarity, this thesis proposes a modified tree edit distance that is metric and polynomially computable and has a natural, intuitive interpretation. Note that the proposed node similarity works even for inter-graph nodes and therefore can be used for graph de-anonymization, network transfer learning, and cross-network mining, among other tasks. The subsequence matching query proposed in this thesis is a framework that can be adopted to index generic sequence and time-series data, including trajectory data and even DNA sequences for subsequence retrieval. For edge sequence queries, this thesis proposes an efficient storage and optimized indexing technique that allows for efficient retrieval of temporal subgraphs that satisfy certain temporal predicates. For this problem, this thesis develops a lightweight data management engine prototype that can support time-sensitive temporal graph analytics efficiently even on a single PC.

Big data is an area focused on storing, processing and visualizing huge amount of data. Today data is growing faster than ever before. We need to find the right tools and applications and build an environment that can help us to obtain valuable insights from the data. Retail is one of the domains that collects huge amount of transaction data everyday. Retailers need to understand their customer’s purchasing pattern and behavior in order to take better business decisions. Market basket analysis is a field in data mining, that is focused on discovering patterns in retail’s transaction data. Our goal is to find tools and applications that can be used by retailers to quickly understand their data and take better business decisions. Due to the amount and complexity of data, it is not possible to do such activities manually. We witness that trends change very quickly and retailers want to be quick in adapting the change and taking actions. This needs automation of processes and using algorithms that are efficient and fast. In our work, we mine transaction data by modeling the data as graphs. We use clustering algorithms to discover communities (clusters) in the data and then use the clusters for building a recommendation system that can recommend products to customers based on their buying behavior.