The Minnesota Supreme Court in a split decision published Wednesday overturned the murder conviction of a Minneapolis man after finding that a police search for Google location data was too broad. But the justices did not ban law enforcement outright from using so-called geofence warrants.
In April 2021, family members of Manuel Mandujano went to the Fifth Precinct police station in south Minneapolis and reported him missing. After visiting a nearby homeless encampment, they’d learned that Mandujano, 39, had gotten into a fight and was beaten to death with a shovel.
Weeks later, police found Mandujano’s remains in a drainage culvert along a sparsely populated rural road in Castle Rock Township south of Farmington, Minn. To track down a suspect, investigators got what’s known as a geofence warrant.
With a judge’s approval, police asked Google for the location history data from every mobile device that appeared in a 65-by-290-foot area around the culvert during the month after Mandujano went missing.
During oral arguments in 2024, Assistant Hennepin County Attorney Adam Petras told the Minnesota Supreme Court that the initial data that Google sent back was anonymous.
“Geofence warrants at their core are like a surveillance camera trained on a specific, fixed, limited area and are actually less intrusive than cell tower dumps, for an example, which involve the gathering of many identifiable phone numbers,” Petras said.
Google’s data showed that a dozen phones had been present along that stretch of rural road in the week after Mandujano was last seen. Eleven belonged to passersby. But one lingered for 10 minutes and registered its location 46 times, including directly over the culvert.
Without seeking an additional warrant, investigators went back to Google for more geographic data about the phone and found that it had pinged its coordinates at a nearby gas station in the 60 minutes prior to appearing at the culvert.
After getting additional approval from a judge, police obtained the personal data associated with the phone and learned it belonged to Ivan Contreras-Sanchez. The 45-year-old admitted in a law enforcement interview that he took part in the killing. A jury convicted Contreras-Sanchez of second-degree intentional murder, and a judge handed him a 40-year sentence.
During the same Supreme Court arguments, defense attorney Jennifer Workman urged the justices to declare geofence warrants unconstitutionally broad.
“We are asking this court to find that geofence warrants are prohibited general warrants because they permit sweeping intrusions into private protected data without a check on law enforcement’s power,” Workman said.
In a 4-2 decision, the court overturned Contreras-Sanchez’s murder conviction. Justice Sarah Hennesy writes that the geofence warrant gave police “unchecked discretion” to determine which of the initial dozen phones that had appeared at the crime scene would be subject to an expanded search.
At the same time the justices upheld the use of geofence warrants, calling them a “useful tool for law enforcement.”
University of St. Thomas law professor Julie Jonas said the court is standing by the Minnesota Constitution’s expectation of privacy for cell phone location data. Jonas said that the decision means that police across Minnesota will need to be more deliberate when they seek geofence warrants.
“I think now a cautious law enforcement agency will also get a warrant at step two, when they want to expand their search beyond that initial step one search area,” Jonas said.
The case now returns to the lower courts, which are expected to determine if Contreras-Sanchez should get a new trial. He remains in custody.
This is the first time that the Minnesota Supreme Court has addressed the constitutionality of geofence warrants.
Chief Justice Natalie Hudson, Justice Paul Thissen, and Justice Karl Procaccini joined Hennesy in the majority. Justice Theodora Gaïtas did not take part in the case.
In dissent, Justice Anne McKeig questioned the wisdom of deciding the matter before the U.S. Supreme Court decides in a similar case. Justice Gordon Moore III joined McKeig in dissent.
Okello Chatrie, who was convicted of robbing a Virginia bank, is challenging the use of geofence warrants on Fourth Amendment grounds. The high court is scheduled to hear oral arguments in the case on April 27.
Power BI is a set of software services, apps, and connectors that work together to turn disparate data sources into coherent, visually immersive, and interactive insights. Your data could be in the form of an Excel spreadsheet or a hybrid data warehouse that is both on-premises and cloud-based. Power BI makes it simple to connect to your data sources, visualize and uncover what matters, and share your findings with whomever you choose.
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What are Datasets in Power BI?
A dataset is a data collection that you can connect to or import. Power BI allows you to connect to and import all kinds of datasets, allowing you to put everything together in one place. Dataflows can also be used for sourcing the data for Datasets. Workspaces are associated with datasets, and a single dataset can be used in multiple workspaces. We have selected “My workspace” and then the “Datasets + dataflows” tab in the example below
Let us now look into the different types of Datasets in Power BI.
Types of Datasets
Datasets in Power BI are ready to report and visualize the source of data. There are five different types of datasets, each of which can be constructed in one of the following ways:
An existing data model will be connected that is not hosted in a Power BI capability.
Power BI Desktop file needs to be uploaded which includes a model.
Uploading a CSV (comma-separated values) file, or uploading an Excel workbook (Includes one or more Excel tables and/or a workbook data model).
Creating a push dataset using the Power BI service.
Creating streaming or dataset with hybrid streaming using the Power BI service.
Let us now explore different types of Datasets.
1) External-hosted models
Azure Analysis Services and SQL Server Analysis Services are the two types of externally hosted models. Installing the on-premises data gateway, whether on-premises or VM-hosted infrastructure-as-a-service (IaaS), is required to connect to a SQL Server Analysis Services model. A gateway isn’t required for Azure Analysis Services.
When there are existing model investments, such as those that form part of an enterprise data warehouse(EDW), connecting to Analysis Services makes sense. By utilizing the identity of the Power BI report user, Power BI can establish a live connection to Analysis Services, enforcing data permissions. Both tabular models and multidimensional (cubes) are supported by SQL Server Analysis Services. A live connection dataset sends queries to externally hosted models, as demonstrated in the accompanying
2) Power BI Desktop-developed models
A model can be created using Power BI Desktop, a client application for Power BI development. The model is essentially a tabular Analysis Services model. Models can be created by importing data from dataflows and blending it with data from external sources. While the characteristics of how modeling can be accomplished are outside the subject of this article, it’s crucial to note that Power BI Desktop supports three different types, or modes, of models. We are going to discuss the datasets in the coming sections.
Row-Level Security (RLS) can be used in externally hosted models and Power BI desktop models to restrict the amount of data that can be obtained for a certain user. Users in the Salespeople security group, for instance, can only see report data for the sales region(s) to which they’ve been assigned. Roles in RLS can be either static or dynamic. Static roles apply the same filters to all users allocated to the position, whereas dynamic roles filter by the report user.
3) Excel workbook models
The creation of a model is automatic when datasets are created from Excel workbooks or CSV files. To construct model tables, Excel tables, and CSV data are imported, and an Excel workbook data model is translated to produce a Power BI model. In every scenario, data from a file is imported into a model.
4) Push Dataset
A Power BI dataset that can only be created and populated using the Power BI API is known as a push dataset. However, the lack of a good user interface for creating a push dataset restricted its adoption to scenarios where a single table was inhabited with real-time data streaming.
5) Hybrid Streaming Dataset
Real-time streaming in Power BI allows you to stream data and update dashboards in real-time. Real-time data and visuals can be displayed and updated in any Power BI visual or dashboard. Factory sensors, social media sources, service usage metrics, and a variety of other time-sensitive data collectors or transmitters can all be used to collect and transmit streaming data.
Before discussing the steps of creation. It is necessary to know that there are three basic ways to retrieve data in Power BI Desktop that you will use to create your visualizations:
1) Live:
Here you will be connecting to a server that carries all the data. Although no data is sent, the model’s metadata is imported into Power BI Desktop. A query is transmitted to the server when you build visualizations, and it is then executed. The outcomes are then visualized and returned to Desktop. With SQL Server Analysis Services (SSAS) models, whether multidimensional or Tabular, live connections are commonly employed. Power BI Desktop behaves like any other thin client in this scenario, like Excel or Reporting Services (SSRS). It is not possible to make major modifications to the model, but you can add new measurements that will be available in that .pbix file.
2) DirectQuery:
You can make more modifications to the model here than you can with a Live connection. The data is kept on the server, and queries are run on the server, just like in Live. The Power BI Desktop model, for instance, allows for the creation of relationships.
3) Import:
Power Query queries are used to import the data into a Power BI Desktop file (.pbix). The data is compressed highly so it’s feasible to load records in millions into a file on your system. A model, comparable to an SSAS Tabular model, is built behind the scenes. This is the most versatile mode, as it allows you to blend data from any source. However, all data must be loaded into your model, which can take a long time to refresh.
Now, let’s move to create the dataset. Below are the steps which make you comprehend the creation of the Power BI Dataset.
1) A dataset is connected to the .pbix file where it was created one by one. When you first launch PBI Desktop, click “Get Data” to create a new dataset.
Alternatively, you can choose a source from the dropdown menu as shown below:
2) Let’s assume we imported a few tables from the WideWorldImporters SQL Server sample database (The .pbix file can be downloaded here). The tables and their relationships are visible in the Model view:
3) You can view the actual data of one table at a time in the “Data view”.
4) You can create, view, and interact with visualizations built on top of the data and model in the “Report view”.
The dataset is made up of the data as well as the model view. Now, let’s move to the different modes of Dataset available in Power BI.
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Dataset modes in the Power BI
These modes of Dataset in Power BI ascertain whether or not data is imported into the model or retained in the data source. The following are the three Dataset modes in Power BI:
Import
DirectQuery
Composite
1) Import
The most popular mode for developing datasets is the import mode. Because of in-memory querying, this mode provides incredibly quick performance. Modelers can also benefit from design flexibility and support for certain Power BI service capabilities (Quick Insights, Q&A, etc.). It’s the default mode when developing a new Power BI Desktop solution because of these advantages.
It’s crucial to realize that all imported data is saved on disk. When the data is refreshed or queried, it should be fully loaded into the memory of Power BI. Import models can yield very rapid query results once they are in memory. It’s also crucial to note that there’s no such thing as a partially loaded Import model in memory. An Import model can also integrate data from any number of supported data source types. The following image illustrates it.
2) DirectQuery
Import mode can be replaced by DirectQuery mode. Data is not imported into models created in DirectQuery mode. Instead, they are made up entirely of metadata that defines the model’s structure. If the model is queried, data is retrieved by using the native queries from the underlying data source.
3) Composite
The composite mode can blend DirectQuery and Import modes, or integrate multiple data sources for DirectQuery. The storage mode for every model table can be configured for models created in Composite mode. Calculated tables (defined with DAX) can also be used in this mode.
Import and DirectQuery modes are used in composite models to give you the best of both modes. They can blend the high query performance of in-memory models with the capacity to access near real-time data from data sources when set properly.
Conclusion: We have successfully learned that Power BI lets you connect various datasets for importing and bringing them all together in one place. In this blog, we explored the topics of Datasets in Power BI in a systematic flow by understanding Power BI, then Datasets in Power BI, different types of Datasets and models used for reporting and visualizing data, creating a Dataset for connecting files, and various modes of Datasets in Power BI.
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